From 424b1ec13558cd059504161b1c7a25236f1599a4 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 14 Nov 2025 18:23:53 +0100 Subject: [PATCH 01/51] add first tuning simulation --- monte-cover/src/montecover/irm/__init__.py | 2 + .../src/montecover/irm/irm_ate_tune.py | 166 ++++++++++++++++++ results/irm/irm_ate_tune_config.yml | 25 +++ results/irm/irm_ate_tune_coverage.csv | 5 + results/irm/irm_ate_tune_metadata.csv | 2 + scripts/irm/irm_ate_tune.py | 13 ++ scripts/irm/irm_ate_tune_config.yml | 29 +++ 7 files changed, 242 insertions(+) create mode 100644 monte-cover/src/montecover/irm/irm_ate_tune.py create mode 100644 results/irm/irm_ate_tune_config.yml create mode 100644 results/irm/irm_ate_tune_coverage.csv create mode 100644 results/irm/irm_ate_tune_metadata.csv create mode 100644 scripts/irm/irm_ate_tune.py create mode 100644 scripts/irm/irm_ate_tune_config.yml diff --git a/monte-cover/src/montecover/irm/__init__.py b/monte-cover/src/montecover/irm/__init__.py index 6c097267..2762182e 100644 --- a/monte-cover/src/montecover/irm/__init__.py +++ b/monte-cover/src/montecover/irm/__init__.py @@ -5,6 +5,7 @@ from montecover.irm.cvar import CVARCoverageSimulation from montecover.irm.iivm_late import IIVMLATECoverageSimulation from montecover.irm.irm_ate import IRMATECoverageSimulation +from montecover.irm.irm_ate_tune import IRMATETuningCoverageSimulation from montecover.irm.irm_ate_sensitivity import IRMATESensitivityCoverageSimulation from montecover.irm.irm_atte import IRMATTECoverageSimulation from montecover.irm.irm_atte_sensitivity import IRMATTESensitivityCoverageSimulation @@ -18,6 +19,7 @@ "APOSCoverageSimulation", "CVARCoverageSimulation", "IRMATECoverageSimulation", + "IRMATETuningCoverageSimulation", "IIVMLATECoverageSimulation", "IRMATESensitivityCoverageSimulation", "IRMATTECoverageSimulation", diff --git a/monte-cover/src/montecover/irm/irm_ate_tune.py b/monte-cover/src/montecover/irm/irm_ate_tune.py new file mode 100644 index 00000000..d2f80fde --- /dev/null +++ b/monte-cover/src/montecover/irm/irm_ate_tune.py @@ -0,0 +1,166 @@ +from typing import Any, Dict, Optional +import optuna + +import doubleml as dml +from doubleml.irm.datasets import make_irm_data + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class IRMATETuningCoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLIRM for ATE estimation with hyperparameter tuning.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + + # tuning specific settings + # parameter space for the outcome regression tuning + def ml_g_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), + 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + } + + # parameter space for the propensity score tuning + def ml_m_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), + 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + } + + self._param_space = { + 'ml_g': ml_g_params, + 'ml_m': ml_m_params + } + + self._optuna_settings = { + 'n_trials': 500, + 'show_progress_bar': False, + 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert "learners" in self.dml_parameters, "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + self.oracle_values["theta"] = self.dgp_parameters["theta"] + + def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + + # Model + dml_model = dml.DoubleMLIRM( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + ) + dml_model.fit() + + dml_model_tuned = dml.DoubleMLIRM( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + ) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + dml_model_tuned.fit() + + result = { + "coverage": [], + } + for model in [dml_model, dml_model_tuned]: + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=model.coef, + oracle_thetas=self.oracle_values["theta"], + confint=model.confint(level=level), + joint_confint=None, + ) + + # add parameters to the result + for res_metric in level_result.values(): + res_metric.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "level": level, + "Tuned": model is dml_model_tuned, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner g", "Learner m", "level", "Tuned"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData: + """Generate data for the simulation.""" + data = make_irm_data( + theta=dgp_params["theta"], + n_obs=dgp_params["n_obs"], + dim_x=dgp_params["dim_x"], + return_type="DataFrame", + ) + dml_data = dml.DoubleMLData(data, "y", "d") + return dml_data diff --git a/results/irm/irm_ate_tune_config.yml b/results/irm/irm_ate_tune_config.yml new file mode 100644 index 00000000..a26fbd76 --- /dev/null +++ b/results/irm/irm_ate_tune_config.yml @@ -0,0 +1,25 @@ +simulation_parameters: + repetitions: 100 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + theta: + - 0.5 + n_obs: + - 1000 + dim_x: + - 5 +learner_definitions: + lgbmr: &id001 + name: LGBM Regr. + lgbmc: &id002 + name: LGBM Clas. +dml_parameters: + learners: + - ml_g: *id001 + ml_m: *id002 +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/irm/irm_ate_tune_coverage.csv b/results/irm/irm_ate_tune_coverage.csv new file mode 100644 index 00000000..3127ca7f --- /dev/null +++ b/results/irm/irm_ate_tune_coverage.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.98,1.917963098754478,0.3759301980407579,100 +LGBM Regr.,LGBM Clas.,0.9,True,0.9,0.3286032416773015,0.08161915599017401,100 +LGBM Regr.,LGBM Clas.,0.95,False,0.99,2.285393992292617,0.3759301980407579,100 +LGBM Regr.,LGBM Clas.,0.95,True,0.94,0.3915549130558737,0.08161915599017401,100 diff --git a/results/irm/irm_ate_tune_metadata.csv b/results/irm/irm_ate_tune_metadata.csv new file mode 100644 index 00000000..e40688fa --- /dev/null +++ b/results/irm/irm_ate_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.11.dev0,IRMATETuningCoverageSimulation,2025-11-14 18:20,22.557860120137533,3.12.9,scripts/irm/irm_ate_tune_config.yml diff --git a/scripts/irm/irm_ate_tune.py b/scripts/irm/irm_ate_tune.py new file mode 100644 index 00000000..675bcfa8 --- /dev/null +++ b/scripts/irm/irm_ate_tune.py @@ -0,0 +1,13 @@ +from montecover.irm import IRMATETuningCoverageSimulation + +# Create and run simulation with config file +sim = IRMATETuningCoverageSimulation( + config_file="scripts/irm/irm_ate_tune_config.yml", + log_level="INFO", + log_file="logs/irm/irm_ate_tune_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/irm/", file_prefix="irm_ate_tune") + +# Save config file for reproducibility +sim.save_config("results/irm/irm_ate_tune_config.yml") diff --git a/scripts/irm/irm_ate_tune_config.yml b/scripts/irm/irm_ate_tune_config.yml new file mode 100644 index 00000000..3fc45050 --- /dev/null +++ b/scripts/irm/irm_ate_tune_config.yml @@ -0,0 +1,29 @@ +# Simulation parameters for IRM ATE Coverage with Tuning + +simulation_parameters: + repetitions: 100 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + theta: [0.5] # Treatment effect + n_obs: [1000] # Sample size + dim_x: [5] # Number of covariates + +# Define reusable learner configurations +learner_definitions: + lgbmr: &lgbmr + name: "LGBM Regr." + + lgbmc: &lgbmc + name: "LGBM Clas." + +dml_parameters: + learners: + - ml_g: *lgbmr + ml_m: *lgbmc + + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From 445c30f030d66ad123c30d907a0ddfc49e0e82fc Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 17 Nov 2025 17:06:21 +0100 Subject: [PATCH 02/51] add plr tuning example --- monte-cover/src/montecover/plm/__init__.py | 2 + .../src/montecover/plm/plr_ate_tune.py | 172 ++++++++++++++++++ results/plm/plr_ate_tune_config.yml | 25 +++ results/plm/plr_ate_tune_coverage.csv | 5 + results/plm/plr_ate_tune_metadata.csv | 2 + scripts/plm/plr_ate_tune.py | 13 ++ scripts/plm/plr_ate_tune_config.yml | 28 +++ 7 files changed, 247 insertions(+) create mode 100644 monte-cover/src/montecover/plm/plr_ate_tune.py create mode 100644 results/plm/plr_ate_tune_config.yml create mode 100644 results/plm/plr_ate_tune_coverage.csv create mode 100644 results/plm/plr_ate_tune_metadata.csv create mode 100644 scripts/plm/plr_ate_tune.py create mode 100644 scripts/plm/plr_ate_tune_config.yml diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py index 167b36d8..3b51c6ce 100644 --- a/monte-cover/src/montecover/plm/__init__.py +++ b/monte-cover/src/montecover/plm/__init__.py @@ -2,6 +2,7 @@ from montecover.plm.pliv_late import PLIVLATECoverageSimulation from montecover.plm.plr_ate import PLRATECoverageSimulation +from montecover.plm.plr_ate_tune import PLRATETuningCoverageSimulation from montecover.plm.plr_ate_sensitivity import PLRATESensitivityCoverageSimulation from montecover.plm.plr_cate import PLRCATECoverageSimulation from montecover.plm.plr_gate import PLRGATECoverageSimulation @@ -12,4 +13,5 @@ "PLRGATECoverageSimulation", "PLRCATECoverageSimulation", "PLRATESensitivityCoverageSimulation", + "PLRATETuningCoverageSimulation", ] diff --git a/monte-cover/src/montecover/plm/plr_ate_tune.py b/monte-cover/src/montecover/plm/plr_ate_tune.py new file mode 100644 index 00000000..ecf19a1f --- /dev/null +++ b/monte-cover/src/montecover/plm/plr_ate_tune.py @@ -0,0 +1,172 @@ +from typing import Any, Dict, Optional +import optuna + +import doubleml as dml +from doubleml.plm.datasets import make_plr_CCDDHNR2018 + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class PLRATETuningCoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + + # tuning specific settings + # parameter space for the outcome regression tuning + def ml_l_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), + 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + } + + # parameter space for the propensity score tuning + def ml_m_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), + 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + } + + self._param_space = { + 'ml_l': ml_l_params, + 'ml_m': ml_m_params + } + + self._optuna_settings = { + 'n_trials': 500, + 'show_progress_bar': False, + 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert "learners" in self.dml_parameters, "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + self.oracle_values["theta"] = self.dgp_parameters["theta"] + + def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + score = dml_params["score"] + + # Model + dml_model = dml.DoubleMLPLR( + obj_dml_data=dml_data, + ml_l=ml_g, + ml_m=ml_m, + ml_g=ml_g if score == "IV-type" else None, + score=score, + ) + dml_model.fit() + + dml_model_tuned = dml.DoubleMLPLR( + obj_dml_data=dml_data, + ml_l=ml_g, + ml_m=ml_m, + ml_g=ml_g if score == "IV-type" else None, + score=score, + ) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + dml_model_tuned.fit() + + result = { + "coverage": [], + } + for model in [dml_model, dml_model_tuned]: + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=model.coef, + oracle_thetas=self.oracle_values["theta"], + confint=model.confint(level=level), + joint_confint=None, + ) + + # add parameters to the result + for res in level_result.values(): + res.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "Score": score, + "level": level, + "Tuned": model is dml_model_tuned, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner g", "Learner m", "Score", "level", "Tuned"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: + """Generate data for the simulation.""" + data = make_plr_CCDDHNR2018( + alpha=dgp_params["theta"], + n_obs=dgp_params["n_obs"], + dim_x=dgp_params["dim_x"], + return_type="DataFrame", + ) + dml_data = dml.DoubleMLData(data, "y", "d") + return dml_data diff --git a/results/plm/plr_ate_tune_config.yml b/results/plm/plr_ate_tune_config.yml new file mode 100644 index 00000000..2e8f0592 --- /dev/null +++ b/results/plm/plr_ate_tune_config.yml @@ -0,0 +1,25 @@ +simulation_parameters: + repetitions: 100 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + theta: + - 0.5 + n_obs: + - 500 + dim_x: + - 20 +learner_definitions: + lgbm: &id001 + name: LGBM Regr. +dml_parameters: + learners: + - ml_g: *id001 + ml_m: *id001 + score: + - partialling out +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/plm/plr_ate_tune_coverage.csv b/results/plm/plr_ate_tune_coverage.csv new file mode 100644 index 00000000..59449592 --- /dev/null +++ b/results/plm/plr_ate_tune_coverage.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,Score,level,Tuned,Coverage,CI Length,Bias,repetition +LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.84,0.1476322636407647,0.0419955778328819,100 +LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.9,0.14472954691929252,0.03509689726085052,100 +LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.91,0.17591469231721352,0.0419955778328819,100 +LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.95,0.17245589200927858,0.03509689726085052,100 diff --git a/results/plm/plr_ate_tune_metadata.csv b/results/plm/plr_ate_tune_metadata.csv new file mode 100644 index 00000000..9686d442 --- /dev/null +++ b/results/plm/plr_ate_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.11.dev0,PLRATETuningCoverageSimulation,2025-11-17 15:23,25.530170826117196,3.12.9,scripts/plm/plr_ate_tune_config.yml diff --git a/scripts/plm/plr_ate_tune.py b/scripts/plm/plr_ate_tune.py new file mode 100644 index 00000000..7c057d3b --- /dev/null +++ b/scripts/plm/plr_ate_tune.py @@ -0,0 +1,13 @@ +from montecover.plm import PLRATETuningCoverageSimulation + +# Create and run simulation with config file +sim = PLRATETuningCoverageSimulation( + config_file="scripts/plm/plr_ate_tune_config.yml", + log_level="INFO", + log_file="logs/plm/plr_ate_tune_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/plm/", file_prefix="plr_ate_tune") + +# Save config file for reproducibility +sim.save_config("results/plm/plr_ate_tune_config.yml") diff --git a/scripts/plm/plr_ate_tune_config.yml b/scripts/plm/plr_ate_tune_config.yml new file mode 100644 index 00000000..e3998ceb --- /dev/null +++ b/scripts/plm/plr_ate_tune_config.yml @@ -0,0 +1,28 @@ +# Simulation parameters for PLR ATE Coverage + +simulation_parameters: + repetitions: 100 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + theta: [0.5] # Treatment effect + n_obs: [500] # Sample size + dim_x: [20] # Number of covariates + +# Define reusable learner configurations +learner_definitions: + lgbm: &lgbm + name: "LGBM Regr." + +dml_parameters: + learners: + - ml_g: *lgbm + ml_m: *lgbm + + + score: ["partialling out"] + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From df3d01f2718a031e34082c92f4d286c846f11e39 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 24 Nov 2025 11:07:36 +0100 Subject: [PATCH 03/51] Fix links and standardize section titles in coverage documentation --- doc/irm/apo.qmd | 6 ++-- doc/irm/iivm.qmd | 4 +-- doc/irm/irm.qmd | 70 ++++++++++++++++++++++++++++++++++++++++---- doc/irm/irm_cate.qmd | 4 +-- doc/irm/irm_gate.qmd | 4 +-- 5 files changed, 74 insertions(+), 14 deletions(-) diff --git a/doc/irm/apo.qmd b/doc/irm/apo.qmd index 376f0831..de63d456 100644 --- a/doc/irm/apo.qmd +++ b/doc/irm/apo.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## APO Pointwise Coverage -The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/api.html#datasets-module)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. +The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. ::: {.callout-note title="Metadata" collapse="true"} @@ -80,7 +80,7 @@ generate_and_show_styled_table( ## APOS Coverage -The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/api.html#datasets-module)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. +The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). @@ -136,7 +136,7 @@ generate_and_show_styled_table( ## Causal Contrast Coverage -The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/api.html#datasets-module)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. +The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). diff --git a/doc/irm/iivm.qmd b/doc/irm/iivm.qmd index 7dd53c28..d18e34a8 100644 --- a/doc/irm/iivm.qmd +++ b/doc/irm/iivm.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## LATE Coverage +## Coverage -The simulations are based on the the [make_iivm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_iivm_data.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. +The simulations are based on the the [make_iivm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_iivm_data.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. ::: {.callout-note title="Metadata" collapse="true"} diff --git a/doc/irm/irm.qmd b/doc/irm/irm.qmd index a25087c1..db4b53fb 100644 --- a/doc/irm/irm.qmd +++ b/doc/irm/irm.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATE Coverage +## Coverage -The simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_irm_data.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. +The simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_irm_data.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. ::: {.callout-note title="Metadata" collapse="true"} @@ -37,6 +37,8 @@ print(metadata_df.T.to_string(header=False)) ::: +### ATE + ```{python} #| echo: false @@ -78,9 +80,9 @@ generate_and_show_styled_table( ``` -## ATTE Coverage +### ATTE -As for the ATE, the simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_irm_data.html)-DGP with $500$ observations. +As for the ATE, the simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_irm_data.html)-DGP with $500$ observations. ::: {.callout-note title="Metadata" collapse="true"} @@ -135,7 +137,7 @@ generate_and_show_styled_table( ## Sensitivity -The simulations are based on the the [make_confounded_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_confounded_irm_data.html#doubleml.datasets.make_confounded_irm_data)-DGP with $5,000$ observations. Since the DGP includes an unobserved confounder, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter. +The simulations are based on the the [make_confounded_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_confounded_irm_data.html#doubleml.datasets.make_confounded_irm_data)-DGP with $5,000$ observations. Since the DGP includes an unobserved confounder, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter. The confounding is set such that both sensitivity parameters are approximately $cf_y=cf_d=0.1$, such that the robustness value $RV$ should be approximately $10\%$. Further, the corresponding confidence intervals are one-sided (since the direction of the bias is unkown), such that only one side should approximate the corresponding coverage level (here only the lower coverage is relevant since the bias is positive). Remark that for the coverage level the value of $\rho$ has to be correctly specified, such that the coverage level will be generally (significantly) larger than the nominal level under the conservative choice of $|\rho|=1$. @@ -245,3 +247,61 @@ generate_and_show_styled_table( coverage_highlight_cols=coverage_highlight_cols_sens ) ``` + + +## Tuning + +The simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_irm_data.html)-DGP with $1000$ observations. This is only an example as the untuned version just relies on the default configuration. + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/irm/irm_ate_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +### ATE + +```{python} +#| echo: false + +# set up data +df_ate_tune_cov = pd.read_csv("../../results/irm/irm_ate_tune_coverage.csv", index_col=None) + +assert df_ate_tune_cov["repetition"].nunique() == 1 +n_rep_ate_tune_cov = df_ate_tune_cov["repetition"].unique()[0] + +display_columns_ate_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage",] +``` + + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_ate_tune_cov, + filters={"level": 0.95}, + display_cols=display_columns_ate_tune_cov, + n_rep=n_rep_ate_cov, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` + + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_ate_tune_cov, + filters={"level": 0.9}, + display_cols=display_columns_ate_tune_cov, + n_rep=n_rep_ate_cov, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` \ No newline at end of file diff --git a/doc/irm/irm_cate.qmd b/doc/irm/irm_cate.qmd index df2d3c67..0e979927 100644 --- a/doc/irm/irm_cate.qmd +++ b/doc/irm/irm_cate.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## CATE Coverage +## Coverage -The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE IRM Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). +The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.irm.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE IRM Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals). diff --git a/doc/irm/irm_gate.qmd b/doc/irm/irm_gate.qmd index 9224fae3..3eaeae69 100644 --- a/doc/irm/irm_gate.qmd +++ b/doc/irm/irm_gate.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## GATE Coverage +## Coverage -The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE IRM Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). +The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE IRM Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals). From a1c474b5529c93633a4664ecc19c55de7af1828e Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 24 Nov 2025 11:25:25 +0100 Subject: [PATCH 04/51] Standardize section titles to "Coverage" in multiple documentation files --- doc/plm/lplr.qmd | 2 +- doc/plm/pliv.qmd | 4 ++-- doc/plm/plr.qmd | 55 ++++++++++++++++++++++++++++++++++++++++---- doc/plm/plr_cate.qmd | 4 ++-- doc/plm/plr_gate.qmd | 4 ++-- 5 files changed, 58 insertions(+), 11 deletions(-) diff --git a/doc/plm/lplr.qmd b/doc/plm/lplr.qmd index b310ce17..9a0eece9 100644 --- a/doc/plm/lplr.qmd +++ b/doc/plm/lplr.qmd @@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATE Coverage +## Coverage The simulations are based on the the [make_lplr_LZZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_lplr_LZZ2020.html)-DGP with $500$ observations. diff --git a/doc/plm/pliv.qmd b/doc/plm/pliv.qmd index eb3b455d..89652428 100644 --- a/doc/plm/pliv.qmd +++ b/doc/plm/pliv.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## LATE Coverage +## Coverage -The simulations are based on the the [make_pliv_CHS2015](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_pliv_CHS2015.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso is a nearly optimal choice for the nuisance estimation. +The simulations are based on the the [make_pliv_CHS2015](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_pliv_CHS2015.html)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso is a nearly optimal choice for the nuisance estimation. ::: {.callout-note title="Metadata" collapse="true"} diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd index f9e93043..23552f37 100644 --- a/doc/plm/plr.qmd +++ b/doc/plm/plr.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATE Coverage +## Coverage -The simulations are based on the the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations. +The simulations are based on the the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations. ::: {.callout-note title="Metadata" collapse="true"} @@ -113,9 +113,9 @@ generate_and_show_styled_table( ) ``` -## ATE Sensitivity +## Sensitivity -The simulations are based on the the [make_confounded_plr_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_confounded_plr_data.html)-DGP with $1000$ observations as highlighted in the [Example Gallery](https://docs.doubleml.org/stable/examples/py_double_ml_sensitivity.html#). As the DGP is nonlinear, we will only use corresponding learners. Since the DGP includes unobserved confounders, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter. +The simulations are based on the the [make_confounded_plr_data](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_confounded_plr_data.html)-DGP with $1000$ observations as highlighted in the [Example Gallery](https://docs.doubleml.org/stable/examples/py_double_ml_sensitivity.html#). As the DGP is nonlinear, we will only use corresponding learners. Since the DGP includes unobserved confounders, we would expect a bias in the ATE estimates, leading to low coverage of the true parameter. Both sensitivity parameters are set to $cf_y=cf_d=0.1$, such that the robustness value $RV$ should be approximately $10\%$. Further, the corresponding confidence intervals are one-sided (since the direction of the bias is unkown), such that only one side should approximate the corresponding coverage level (here only the upper coverage is relevant since the bias is positive). Remark that for the coverage level the value of $\rho$ has to be correctly specified, such that the coverage level will be generally (significantly) larger than the nominal level under the conservative choice of $|\rho|=1$. @@ -208,3 +208,50 @@ generate_and_show_styled_table( coverage_highlight_cols=["Coverage", "Coverage (Upper)"] ) ``` + +## Tuning + +The simulations are based on the the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration. + +```{python} +#| echo: false + +# set up data +df_tune_cov = pd.read_csv("../../results/plm/plr_ate_tune_coverage.csv", index_col=None) + +assert df_tune_cov["repetition"].nunique() == 1 +n_rep_tune_cov = df_tune_cov["repetition"].unique()[0] + +display_columns_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage",] +``` + + +### Partialling out + +```{python} +# | echo: false + +generate_and_show_styled_table( + main_df=df_tune_cov, + filters={"level": 0.95, "Score": "partialling out"}, + display_cols=display_columns_tune_cov, + n_rep=n_rep_tune_cov, + level_col="level", + rename_map={"Learner g": "Learner l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_tune_cov, + filters={"level": 0.9, "Score": "partialling out"}, + display_cols=display_columns_tune_cov, + n_rep=n_rep_tune_cov, + level_col="level", + rename_map={"Learner g": "Learner l"}, + coverage_highlight_cols=["Coverage"] +) +``` \ No newline at end of file diff --git a/doc/plm/plr_cate.qmd b/doc/plm/plr_cate.qmd index 15810255..6ac7d99a 100644 --- a/doc/plm/plr_cate.qmd +++ b/doc/plm/plr_cate.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## CATE Coverage +## Coverage -The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). +The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_heterogeneous_data.html)-DGP with $2000$ observations. The groups are defined based on the first covariate, analogously to the [CATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_cate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals). diff --git a/doc/plm/plr_gate.qmd b/doc/plm/plr_gate.qmd index d32bd4ef..52da0229 100644 --- a/doc/plm/plr_gate.qmd +++ b/doc/plm/plr_gate.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## GATE Coverage +## Coverage -The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). +The simulations are based on the the [make_heterogeneous_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_heterogeneous_data.html)-DGP with $500$ observations. The groups are defined based on the first covariate, analogously to the [GATE PLR Example](https://docs.doubleml.org/stable/examples/py_double_ml_gate_plr.html), but rely on [LightGBM](https://lightgbm.readthedocs.io/en/latest/index.html) to estimate nuisance elements (due to time constraints). The non-uniform results (coverage, ci length and bias) refer to averaged values over all groups (point-wise confidende intervals). From c450065c79e1d8a95ef8b896ebb34b6d78ec9942 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 24 Nov 2025 11:27:19 +0100 Subject: [PATCH 05/51] Update documentation links to stable API references in coverage sections --- doc/did/did_cs.qmd | 2 +- doc/did/did_cs_multi.qmd | 4 ++-- doc/did/did_pa.qmd | 2 +- doc/did/did_pa_multi.qmd | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/did/did_cs.qmd b/doc/did/did_cs.qmd index eab72ec2..e23c9e6e 100644 --- a/doc/did/did_cs.qmd +++ b/doc/did/did_cs.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## ATTE Coverage -The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). +The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). ::: {.callout-note title="Metadata" collapse="true"} diff --git a/doc/did/did_cs_multi.qmd b/doc/did/did_cs_multi.qmd index fba42d58..a57fa8ee 100644 --- a/doc/did/did_cs_multi.qmd +++ b/doc/did/did_cs_multi.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## ATTE Coverage -The simulations are based on the [make_did_cs_CS2021](https://docs.doubleml.org/dev/api/generated/doubleml.did.datasets.make_did_cs_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: +The simulations are based on the [make_did_cs_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_cs_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: - Type 1: Linear outcome model and treatment assignment - Type 4: Nonlinear outcome model and treatment assignment @@ -112,7 +112,7 @@ generate_and_show_styled_table( ## Aggregated Effects -These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did). +These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did). The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidence intervals). diff --git a/doc/did/did_pa.qmd b/doc/did/did_pa.qmd index 94f16ed8..2f81bbd4 100644 --- a/doc/did/did_pa.qmd +++ b/doc/did/did_pa.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## ATTE Coverage -The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). +The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). ::: {.callout-note title="Metadata" collapse="true"} diff --git a/doc/did/did_pa_multi.qmd b/doc/did/did_pa_multi.qmd index b004299f..6f306800 100644 --- a/doc/did/did_pa_multi.qmd +++ b/doc/did/did_pa_multi.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## ATTE Coverage -The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/dev/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: +The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: - Type 1: Linear outcome model and treatment assignment - Type 4: Nonlinear outcome model and treatment assignment @@ -112,7 +112,7 @@ generate_and_show_styled_table( ## Aggregated Effects -These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/dev/guide/models.html#difference-in-differences-models-did). +These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did). The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). From 93f68ad56f38d27392659a8a7a89fe0a845d7251 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 24 Nov 2025 11:29:32 +0100 Subject: [PATCH 06/51] Standardize section titles to "Coverage" in multiple documentation files --- doc/did/did_cs.qmd | 2 +- doc/did/did_cs_multi.qmd | 2 +- doc/did/did_pa.qmd | 2 +- doc/did/did_pa_multi.qmd | 2 +- doc/ssm/ssm_mar.qmd | 4 ++-- doc/ssm/ssm_nonignorable.qmd | 4 ++-- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/did/did_cs.qmd b/doc/did/did_cs.qmd index e23c9e6e..39047753 100644 --- a/doc/did/did_cs.qmd +++ b/doc/did/did_cs.qmd @@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATTE Coverage +## Coverage The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). diff --git a/doc/did/did_cs_multi.qmd b/doc/did/did_cs_multi.qmd index a57fa8ee..81b9441b 100644 --- a/doc/did/did_cs_multi.qmd +++ b/doc/did/did_cs_multi.qmd @@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATTE Coverage +## Coverage The simulations are based on the [make_did_cs_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_cs_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: diff --git a/doc/did/did_pa.qmd b/doc/did/did_pa.qmd index 2f81bbd4..2a9a58f6 100644 --- a/doc/did/did_pa.qmd +++ b/doc/did/did_pa.qmd @@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATTE Coverage +## Coverage The simulations are based on the the [make_did_SZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_SZ2020.html)-DGP with $1000$ observations. Learners are only set to boosting, due to time constraints (and the nonlinearity of some of the DGPs). diff --git a/doc/did/did_pa_multi.qmd b/doc/did/did_pa_multi.qmd index 6f306800..22cb5de7 100644 --- a/doc/did/did_pa_multi.qmd +++ b/doc/did/did_pa_multi.qmd @@ -22,7 +22,7 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATTE Coverage +## Coverage The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: diff --git a/doc/ssm/ssm_mar.qmd b/doc/ssm/ssm_mar.qmd index a396fa4a..36334158 100644 --- a/doc/ssm/ssm_mar.qmd +++ b/doc/ssm/ssm_mar.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATE Coverage +## Coverage -The simulations are based on the [make_ssm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_ssm_data.html)-DGP with $500$ observations. The simulation considers data under [missingness at random](https://docs.doubleml.org/stable/guide/models.html#missingness-at-random). +The simulations are based on the [make_ssm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_ssm_data.html)-DGP with $500$ observations. The simulation considers data under [missingness at random](https://docs.doubleml.org/stable/guide/models.html#missingness-at-random). ::: {.callout-note title="Metadata" collapse="true"} diff --git a/doc/ssm/ssm_nonignorable.qmd b/doc/ssm/ssm_nonignorable.qmd index 8eff76b9..8b8be403 100644 --- a/doc/ssm/ssm_nonignorable.qmd +++ b/doc/ssm/ssm_nonignorable.qmd @@ -22,9 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## ATE Coverage +## Coverage -The simulations are based on the [make_ssm_data](https://docs.doubleml.org/stable/api/generated/doubleml.datasets.make_ssm_data.html)-DGP with $500$ observations. The simulation considers data with [nonignorable nonresponse](https://docs.doubleml.org/stable/guide/models.html#nonignorable-nonresponse). +The simulations are based on the [make_ssm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_ssm_data.html)-DGP with $500$ observations. The simulation considers data with [nonignorable nonresponse](https://docs.doubleml.org/stable/guide/models.html#nonignorable-nonresponse). ::: {.callout-note title="Metadata" collapse="true"} From 27fa90e3ee03ad860c07f1fe4568c815cd2025fb Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 24 Nov 2025 15:45:42 +0100 Subject: [PATCH 07/51] update tuning sim --- results/irm/irm_ate_tune_config.yml | 2 +- results/irm/irm_ate_tune_coverage.csv | 8 ++++---- results/irm/irm_ate_tune_metadata.csv | 2 +- results/plm/plr_ate_tune_config.yml | 2 +- results/plm/plr_ate_tune_coverage.csv | 8 ++++---- results/plm/plr_ate_tune_metadata.csv | 2 +- scripts/irm/irm_ate_tune_config.yml | 2 +- scripts/plm/plr_ate_tune_config.yml | 2 +- 8 files changed, 14 insertions(+), 14 deletions(-) diff --git a/results/irm/irm_ate_tune_config.yml b/results/irm/irm_ate_tune_config.yml index a26fbd76..285d88ba 100644 --- a/results/irm/irm_ate_tune_config.yml +++ b/results/irm/irm_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 100 + repetitions: 500 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/irm/irm_ate_tune_coverage.csv b/results/irm/irm_ate_tune_coverage.csv index 3127ca7f..5780228c 100644 --- a/results/irm/irm_ate_tune_coverage.csv +++ b/results/irm/irm_ate_tune_coverage.csv @@ -1,5 +1,5 @@ Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,False,0.98,1.917963098754478,0.3759301980407579,100 -LGBM Regr.,LGBM Clas.,0.9,True,0.9,0.3286032416773015,0.08161915599017401,100 -LGBM Regr.,LGBM Clas.,0.95,False,0.99,2.285393992292617,0.3759301980407579,100 -LGBM Regr.,LGBM Clas.,0.95,True,0.94,0.3915549130558737,0.08161915599017401,100 +LGBM Regr.,LGBM Clas.,0.9,False,0.952,1.8938962143131024,0.38916017267237296,500 +LGBM Regr.,LGBM Clas.,0.9,True,0.82,0.31269268855505145,0.09112340834220442,500 +LGBM Regr.,LGBM Clas.,0.95,False,0.988,2.256716530692214,0.38916017267237296,500 +LGBM Regr.,LGBM Clas.,0.95,True,0.9,0.3725963196693502,0.09112340834220442,500 diff --git a/results/irm/irm_ate_tune_metadata.csv b/results/irm/irm_ate_tune_metadata.csv index e40688fa..1ba129ae 100644 --- a/results/irm/irm_ate_tune_metadata.csv +++ b/results/irm/irm_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMATETuningCoverageSimulation,2025-11-14 18:20,22.557860120137533,3.12.9,scripts/irm/irm_ate_tune_config.yml +0.12.dev0,IRMATETuningCoverageSimulation,2025-11-24 13:07,97.69014709790548,3.12.9,scripts/irm/irm_ate_tune_config.yml diff --git a/results/plm/plr_ate_tune_config.yml b/results/plm/plr_ate_tune_config.yml index 2e8f0592..9893ef17 100644 --- a/results/plm/plr_ate_tune_config.yml +++ b/results/plm/plr_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 100 + repetitions: 500 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/plm/plr_ate_tune_coverage.csv b/results/plm/plr_ate_tune_coverage.csv index 59449592..a6cb5fe8 100644 --- a/results/plm/plr_ate_tune_coverage.csv +++ b/results/plm/plr_ate_tune_coverage.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,level,Tuned,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.84,0.1476322636407647,0.0419955778328819,100 -LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.9,0.14472954691929252,0.03509689726085052,100 -LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.91,0.17591469231721352,0.0419955778328819,100 -LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.95,0.17245589200927858,0.03509689726085052,100 +LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.81,0.1479047026981892,0.04545068026009308,500 +LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.894,0.1451364370890545,0.037424408565466506,500 +LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.898,0.17623932347696217,0.04545068026009308,500 +LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.942,0.1729407315508218,0.037424408565466506,500 diff --git a/results/plm/plr_ate_tune_metadata.csv b/results/plm/plr_ate_tune_metadata.csv index 9686d442..eedfd1ee 100644 --- a/results/plm/plr_ate_tune_metadata.csv +++ b/results/plm/plr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLRATETuningCoverageSimulation,2025-11-17 15:23,25.530170826117196,3.12.9,scripts/plm/plr_ate_tune_config.yml +0.12.dev0,PLRATETuningCoverageSimulation,2025-11-24 15:37,119.77829658587774,3.12.9,scripts/plm/plr_ate_tune_config.yml diff --git a/scripts/irm/irm_ate_tune_config.yml b/scripts/irm/irm_ate_tune_config.yml index 3fc45050..ff0eb6fe 100644 --- a/scripts/irm/irm_ate_tune_config.yml +++ b/scripts/irm/irm_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for IRM ATE Coverage with Tuning simulation_parameters: - repetitions: 100 + repetitions: 500 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 diff --git a/scripts/plm/plr_ate_tune_config.yml b/scripts/plm/plr_ate_tune_config.yml index e3998ceb..df0ecd25 100644 --- a/scripts/plm/plr_ate_tune_config.yml +++ b/scripts/plm/plr_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 100 + repetitions: 500 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 From e21b357961437534bc2f8d636af8144f618829ae Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 26 Nov 2025 11:42:23 +0100 Subject: [PATCH 08/51] first apos tuning sim --- monte-cover/src/montecover/irm/__init__.py | 2 + monte-cover/src/montecover/irm/apos_tune.py | 212 ++++++++++++++++++ .../src/montecover/irm/irm_ate_tune.py | 2 +- results/irm/apos_tune_causal_contrast.csv | 5 + results/irm/apos_tune_config.yml | 31 +++ results/irm/apos_tune_coverage.csv | 5 + results/irm/apos_tune_metadata.csv | 2 + scripts/irm/apos_tune.py | 13 ++ scripts/irm/apos_tune_config.yml | 33 +++ 9 files changed, 304 insertions(+), 1 deletion(-) create mode 100644 monte-cover/src/montecover/irm/apos_tune.py create mode 100644 results/irm/apos_tune_causal_contrast.csv create mode 100644 results/irm/apos_tune_config.yml create mode 100644 results/irm/apos_tune_coverage.csv create mode 100644 results/irm/apos_tune_metadata.csv create mode 100644 scripts/irm/apos_tune.py create mode 100644 scripts/irm/apos_tune_config.yml diff --git a/monte-cover/src/montecover/irm/__init__.py b/monte-cover/src/montecover/irm/__init__.py index 2762182e..c54e9991 100644 --- a/monte-cover/src/montecover/irm/__init__.py +++ b/monte-cover/src/montecover/irm/__init__.py @@ -2,6 +2,7 @@ from montecover.irm.apo import APOCoverageSimulation from montecover.irm.apos import APOSCoverageSimulation +from montecover.irm.apos_tune import APOSTuningCoverageSimulation from montecover.irm.cvar import CVARCoverageSimulation from montecover.irm.iivm_late import IIVMLATECoverageSimulation from montecover.irm.irm_ate import IRMATECoverageSimulation @@ -17,6 +18,7 @@ __all__ = [ "APOCoverageSimulation", "APOSCoverageSimulation", + "APOSTuningCoverageSimulation", "CVARCoverageSimulation", "IRMATECoverageSimulation", "IRMATETuningCoverageSimulation", diff --git a/monte-cover/src/montecover/irm/apos_tune.py b/monte-cover/src/montecover/irm/apos_tune.py new file mode 100644 index 00000000..447b7a2b --- /dev/null +++ b/monte-cover/src/montecover/irm/apos_tune.py @@ -0,0 +1,212 @@ +from typing import Any, Dict, Optional +import optuna + +import doubleml as dml +import numpy as np +import pandas as pd +from doubleml.irm.datasets import make_irm_data_discrete_treatments + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class APOSTuningCoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLAPOs for APO estimation with tuning.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + + # tuning specific settings + # parameter space for the outcome regression tuning + def ml_g_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 200, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 50, step=5), + 'max_depth': 5, + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-3, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-3, 10.0, log=True), + } + + # parameter space for the propensity score tuning + def ml_m_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 200, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 50, step=5), + 'max_depth': 5, + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-3, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-3, 10.0, log=True), + } + + self._param_space = { + 'ml_g': ml_g_params, + 'ml_m': ml_m_params + } + + self._optuna_settings = { + 'n_trials': 200, + 'show_progress_bar': False, + 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert "learners" in self.dml_parameters, "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + n_levels = self.dgp_parameters["n_levels"][0] + data_apo_oracle = make_irm_data_discrete_treatments( + n_obs=int(1e6), n_levels=n_levels, linear=self.dgp_parameters["linear"][0] + ) + + y0 = data_apo_oracle["oracle_values"]["y0"] + ite = data_apo_oracle["oracle_values"]["ite"] + d = data_apo_oracle["d"] + + average_ites = np.full(n_levels + 1, np.nan) + apos = np.full(n_levels + 1, np.nan) + for i in range(n_levels + 1): + average_ites[i] = np.mean(ite[d == i]) * (i > 0) + apos[i] = np.mean(y0) + average_ites[i] + + ates = np.full(n_levels, np.nan) + for i in range(n_levels): + ates[i] = apos[i + 1] - apos[0] + + self.logger.info(f"Levels and their counts:\n{np.unique(d, return_counts=True)}") + self.logger.info(f"True APOs: {apos}") + self.logger.info(f"True ATEs: {ates}") + + self.oracle_values = dict() + self.oracle_values["apos"] = apos + self.oracle_values["ates"] = ates + + def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + treatment_levels = dml_params["treatment_levels"] + trimming_threshold = dml_params["trimming_threshold"] + + # Model + dml_model = dml.DoubleMLAPOS( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + treatment_levels=treatment_levels, + trimming_threshold=trimming_threshold, + ) + # Tuning + dml_model_tuned = dml.DoubleMLAPOS( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=ml_m, + treatment_levels=treatment_levels, + trimming_threshold=trimming_threshold, + ) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + + result = { + "coverage": [], + "causal_contrast": [], + } + for model in [dml_model, dml_model_tuned]: + model.fit() + model.bootstrap(n_rep_boot=2000) + causal_contrast_model = model.causal_contrast(reference_levels=0) + causal_contrast_model.bootstrap(n_rep_boot=2000) + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=model.coef, + oracle_thetas=self.oracle_values["apos"], + confint=model.confint(level=level), + joint_confint=model.confint(level=level, joint=True), + ) + level_result["causal_contrast"] = self._compute_coverage( + thetas=causal_contrast_model.thetas, + oracle_thetas=self.oracle_values["ates"], + confint=causal_contrast_model.confint(level=level), + joint_confint=causal_contrast_model.confint(level=level, joint=True), + ) + + # add parameters to the result + for res_metric in level_result.values(): + res_metric.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "level": level, + "Tuned": model is dml_model_tuned, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner g", "Learner m", "level", "Tuned"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "Uniform Coverage": "mean", + "Uniform CI Length": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData: + """Generate data for the simulation.""" + data = make_irm_data_discrete_treatments( + n_obs=dgp_params["n_obs"], + n_levels=dgp_params["n_levels"], + linear=dgp_params["linear"], + ) + df_apo = pd.DataFrame( + np.column_stack((data["y"], data["d"], data["x"])), + columns=["y", "d"] + ["x" + str(i) for i in range(data["x"].shape[1])], + ) + dml_data = dml.DoubleMLData(df_apo, "y", "d") + return dml_data diff --git a/monte-cover/src/montecover/irm/irm_ate_tune.py b/monte-cover/src/montecover/irm/irm_ate_tune.py index d2f80fde..19d52b24 100644 --- a/monte-cover/src/montecover/irm/irm_ate_tune.py +++ b/monte-cover/src/montecover/irm/irm_ate_tune.py @@ -98,7 +98,7 @@ def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) obj_dml_data=dml_data, ml_g=ml_g, ml_m=ml_m, - ) + ) dml_model_tuned.tune_ml_models( ml_param_space=self._param_space, optuna_settings=self._optuna_settings, diff --git a/results/irm/apos_tune_causal_contrast.csv b/results/irm/apos_tune_causal_contrast.csv new file mode 100644 index 00000000..9c88e6bd --- /dev/null +++ b/results/irm/apos_tune_causal_contrast.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.915,37.253979808129365,8.903634313073434,0.95,44.16923532073818,200 +LGBM Regr.,LGBM Clas.,0.9,True,0.835,4.834836017081165,1.3591722777708926,0.815,5.703081839207788,200 +LGBM Regr.,LGBM Clas.,0.95,False,0.98,44.39085491153563,8.903634313073434,0.985,50.67060073707776,200 +LGBM Regr.,LGBM Clas.,0.95,True,0.915,5.761062449185174,1.3591722777708926,0.895,6.546100271377481,200 diff --git a/results/irm/apos_tune_config.yml b/results/irm/apos_tune_config.yml new file mode 100644 index 00000000..b8d83823 --- /dev/null +++ b/results/irm/apos_tune_config.yml @@ -0,0 +1,31 @@ +simulation_parameters: + repetitions: 200 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + n_obs: + - 500 + n_levels: + - 2 + linear: + - true +learner_definitions: + lgbmr: &id001 + name: LGBM Regr. + lgbmc: &id002 + name: LGBM Clas. +dml_parameters: + treatment_levels: + - - 0 + - 1 + - 2 + trimming_threshold: + - 0.01 + learners: + - ml_g: *id001 + ml_m: *id002 +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/irm/apos_tune_coverage.csv b/results/irm/apos_tune_coverage.csv new file mode 100644 index 00000000..d01ff1f6 --- /dev/null +++ b/results/irm/apos_tune_coverage.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.93,27.82005877126217,6.439524063849977,0.96,35.676118532942496,200 +LGBM Regr.,LGBM Clas.,0.9,True,0.885,6.300962875030208,1.5916287837149021,0.88,7.710244822536492,200 +LGBM Regr.,LGBM Clas.,0.95,False,0.9766666666666667,33.14964465289176,6.439524063849977,0.975,40.33245838311548,200 +LGBM Regr.,LGBM Clas.,0.95,True,0.9466666666666668,7.50806035298818,1.5916287837149021,0.94,8.829514768318946,200 diff --git a/results/irm/apos_tune_metadata.csv b/results/irm/apos_tune_metadata.csv new file mode 100644 index 00000000..57cc25db --- /dev/null +++ b/results/irm/apos_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.12.dev0,APOSTuningCoverageSimulation,2025-11-26 11:38,31.90539586544037,3.12.9,scripts/irm/apos_tune_config.yml diff --git a/scripts/irm/apos_tune.py b/scripts/irm/apos_tune.py new file mode 100644 index 00000000..f7a2a8b0 --- /dev/null +++ b/scripts/irm/apos_tune.py @@ -0,0 +1,13 @@ +from montecover.irm import APOSTuningCoverageSimulation + +# Create and run simulation with config file +sim = APOSTuningCoverageSimulation( + config_file="scripts/irm/apos_tune_config.yml", + log_level="INFO", + log_file="logs/irm/apos_tune_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/irm/", file_prefix="apos_tune") + +# Save config file for reproducibility +sim.save_config("results/irm/apos_tune_config.yml") diff --git a/scripts/irm/apos_tune_config.yml b/scripts/irm/apos_tune_config.yml new file mode 100644 index 00000000..1da3c6cd --- /dev/null +++ b/scripts/irm/apos_tune_config.yml @@ -0,0 +1,33 @@ +# Simulation parameters for APOS Coverage + +simulation_parameters: + repetitions: 200 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + n_obs: [500] # Sample size + n_levels: [2] + linear: [True] + +# Define reusable learner configurations +learner_definitions: + lgbmr: &lgbmr + name: "LGBM Regr." + + + lgbmc: &lgbmc + name: "LGBM Clas." + +dml_parameters: + treatment_levels: [[0, 1, 2]] + trimming_threshold: [0.01] + learners: + - ml_g: *lgbmr + ml_m: *lgbmc + + + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From 46522acd9afde97349df87fb1fb27cc6e71864ce Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 26 Nov 2025 17:54:40 +0100 Subject: [PATCH 09/51] rerun lplr sim and add tuning with 100 reps and 200 trials --- monte-cover/src/montecover/plm/__init__.py | 2 + monte-cover/src/montecover/plm/lplr_ate.py | 6 +- .../src/montecover/plm/lplr_ate_tune.py | 160 ++++++++++++++++++ results/plm/lplr_ate_config.yml | 2 +- results/plm/lplr_ate_coverage.csv | 24 +-- results/plm/lplr_ate_metadata.csv | 2 +- results/plm/lplr_ate_tune_config.yml | 29 ++++ results/plm/lplr_ate_tune_coverage.csv | 9 + results/plm/lplr_ate_tune_metadata.csv | 2 + scripts/plm/lplr_ate_config.yml | 2 +- scripts/plm/lplr_ate_tune.py | 14 ++ scripts/plm/lplr_ate_tune_config.yml | 31 ++++ 12 files changed, 265 insertions(+), 18 deletions(-) create mode 100644 monte-cover/src/montecover/plm/lplr_ate_tune.py create mode 100644 results/plm/lplr_ate_tune_config.yml create mode 100644 results/plm/lplr_ate_tune_coverage.csv create mode 100644 results/plm/lplr_ate_tune_metadata.csv create mode 100644 scripts/plm/lplr_ate_tune.py create mode 100644 scripts/plm/lplr_ate_tune_config.yml diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py index 881a7016..4aa935ba 100644 --- a/monte-cover/src/montecover/plm/__init__.py +++ b/monte-cover/src/montecover/plm/__init__.py @@ -7,6 +7,7 @@ from montecover.plm.plr_cate import PLRCATECoverageSimulation from montecover.plm.plr_gate import PLRGATECoverageSimulation from montecover.plm.lplr_ate import LPLRATECoverageSimulation +from montecover.plm.lplr_ate_tune import LPLRATETuningCoverageSimulation __all__ = [ "PLRATECoverageSimulation", @@ -16,4 +17,5 @@ "PLRATESensitivityCoverageSimulation", "PLRATETuningCoverageSimulation", "LPLRATECoverageSimulation", + "LPLRATETuningCoverageSimulation", ] diff --git a/monte-cover/src/montecover/plm/lplr_ate.py b/monte-cover/src/montecover/plm/lplr_ate.py index da962e32..2d62ee26 100644 --- a/monte-cover/src/montecover/plm/lplr_ate.py +++ b/monte-cover/src/montecover/plm/lplr_ate.py @@ -1,4 +1,3 @@ -import warnings from typing import Any, Dict, Optional import doubleml as dml @@ -46,7 +45,7 @@ def _calculate_oracle_values(self): self.logger.info("Calculating oracle values") self.oracle_values = dict() - self.oracle_values["theta"] = self.dgp_parameters["theta"] + self.oracle_values["theta"] = self.dgp_parameters["alpha"] def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" @@ -64,7 +63,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ml_M=ml_M, ml_t=ml_t, score=score, - error_on_convergence_failure= not self._use_failed_scores,) + error_on_convergence_failure=(not self._use_failed_scores), + ) try: dml_model.fit() diff --git a/monte-cover/src/montecover/plm/lplr_ate_tune.py b/monte-cover/src/montecover/plm/lplr_ate_tune.py new file mode 100644 index 00000000..227339e6 --- /dev/null +++ b/monte-cover/src/montecover/plm/lplr_ate_tune.py @@ -0,0 +1,160 @@ +from typing import Any, Dict, Optional +import optuna + +import doubleml as dml +from doubleml.plm.datasets import make_lplr_LZZ2020 + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class LPLRATETuningCoverageSimulation(BaseSimulation): + """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + use_failed_scores: bool = False, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Calculate oracle values + self._calculate_oracle_values() + self._use_failed_scores = use_failed_scores + + # for simplicity, we use the same parameter space for all learners + def ml_params(trial): + return { + 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), + 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), + 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), + 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + } + + self._param_space = { + 'ml_M': ml_params, + 'ml_t': ml_params, + 'ml_m': ml_params, + 'ml_a': ml_params, + } + + self._optuna_settings = { + 'n_trials': 200, + 'show_progress_bar': False, + 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + assert "learners" in self.dml_parameters, "No learners specified in the config file" + + required_learners = ["ml_m", "ml_M", "ml_t"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + self.oracle_values["theta"] = self.dgp_parameters["alpha"] + + def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + learner_M_name, ml_M = create_learner_from_config(learner_config["ml_M"]) + learner_t_name, ml_t = create_learner_from_config(learner_config["ml_t"]) + score = dml_params["score"] + + model_inputs = { + "obj_dml_data": dml_data, + "ml_m": ml_m, + "ml_M": ml_M, + "ml_t": ml_t, + "score": score, + "error_on_convergence_failure": not self._use_failed_scores, + + } + # Model + dml_model = dml.DoubleMLLPLR(**model_inputs) + dml_model_tuned = dml.DoubleMLLPLR(**model_inputs) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + + result = { + "coverage": [], + } + + for model in [dml_model, dml_model_tuned]: + try: + model.fit() + except RuntimeError as e: + self.logger.info(f"Exception during fit: {e}") + return None + + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["coverage"] = self._compute_coverage( + thetas=model.coef, + oracle_thetas=self.oracle_values["theta"], + confint=model.confint(level=level), + joint_confint=None, + ) + + # add parameters to the result + for res in level_result.values(): + res.update( + { + "Learner m": learner_m_name, + "Learner M": learner_M_name, + "Learner t": learner_t_name, + "Score": score, + "level": level, + "Tuned": model is dml_model_tuned, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + # Group by parameter combinations + groupby_cols = ["Learner m", "Learner M", "Learner t", "Score", "level", "Tuned"] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "repetition": "count", + } + + # Aggregate results (possibly multiple result dfs) + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: + """Generate data for the simulation.""" + return make_lplr_LZZ2020(**dgp_params) diff --git a/results/plm/lplr_ate_config.yml b/results/plm/lplr_ate_config.yml index c7cf40d2..0113545b 100644 --- a/results/plm/lplr_ate_config.yml +++ b/results/plm/lplr_ate_config.yml @@ -4,7 +4,7 @@ simulation_parameters: random_seed: 42 n_jobs: -2 dgp_parameters: - theta: + alpha: - 0.5 n_obs: - 500 diff --git a/results/plm/lplr_ate_coverage.csv b/results/plm/lplr_ate_coverage.csv index 29c3a423..17f7507b 100644 --- a/results/plm/lplr_ate_coverage.csv +++ b/results/plm/lplr_ate_coverage.csv @@ -1,13 +1,13 @@ Learner m,Learner M,Learner t,Score,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,0.872,0.6540916267945179,0.17501445022837125,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,0.928,0.7793982455949509,0.17501445022837125,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,0.88,0.598241346108922,0.15586913796966942,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,0.946,0.7128485314583201,0.15586913796966942,500 -LassoCV,Logistic,LassoCV,instrument,0.9,0.856,0.5890452894815547,0.16482024691605957,500 -LassoCV,Logistic,LassoCV,instrument,0.95,0.924,0.7018907541253692,0.16482024691605957,500 -LassoCV,Logistic,LassoCV,nuisance_space,0.9,0.868,0.5820699058557912,0.1507959338822808,500 -LassoCV,Logistic,LassoCV,nuisance_space,0.95,0.93,0.6935790718815301,0.1507959338822808,500 -RF Regr.,RF Clas.,RF Regr.,instrument,0.9,0.884,0.39484117997902796,0.09883032061915417,500 -RF Regr.,RF Clas.,RF Regr.,instrument,0.95,0.95,0.4704822846799266,0.09883032061915417,500 -RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.9,0.886,0.38499391911236014,0.09772003875711463,500 -RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.95,0.94,0.45874854963578754,0.09772003875711463,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,0.866,0.6573798859045776,0.17600558265832575,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,0.936,0.7833164479942107,0.17600558265832575,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,0.89,0.5881153537384244,0.15332249272864673,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,0.936,0.700782667342079,0.15332249272864673,500 +LassoCV,Logistic,LassoCV,instrument,0.9,0.858,0.5897233516083383,0.16268441455635813,500 +LassoCV,Logistic,LassoCV,instrument,0.95,0.916,0.7026987149834061,0.16268441455635813,500 +LassoCV,Logistic,LassoCV,nuisance_space,0.9,0.8937875751503006,0.576947311075238,0.1492081384708213,499 +LassoCV,Logistic,LassoCV,nuisance_space,0.95,0.9278557114228457,0.6874751237169234,0.1492081384708213,499 +RF Regr.,RF Clas.,RF Regr.,instrument,0.9,0.902,0.39485055228075816,0.09886061010323771,500 +RF Regr.,RF Clas.,RF Regr.,instrument,0.95,0.942,0.4704934524662526,0.09886061010323771,500 +RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.9,0.892,0.38461199091029774,0.09604302638290617,500 +RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.95,0.942,0.4582934541133308,0.09604302638290617,500 diff --git a/results/plm/lplr_ate_metadata.csv b/results/plm/lplr_ate_metadata.csv index 52735907..bc94ba00 100644 --- a/results/plm/lplr_ate_metadata.csv +++ b/results/plm/lplr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,LPLRATECoverageSimulation,2025-11-18 03:13,39.79484195311864,3.12.9,scripts/plm/lplr_ate_config.yml +0.12.dev0,LPLRATECoverageSimulation,2025-11-26 13:24,14.800051196416218,3.12.9,scripts/plm/lplr_ate_config.yml diff --git a/results/plm/lplr_ate_tune_config.yml b/results/plm/lplr_ate_tune_config.yml new file mode 100644 index 00000000..30a6e5c3 --- /dev/null +++ b/results/plm/lplr_ate_tune_config.yml @@ -0,0 +1,29 @@ +simulation_parameters: + repetitions: 100 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + alpha: + - 0.5 + n_obs: + - 500 + dim_x: + - 20 +learner_definitions: + lgbm: &id001 + name: LGBM Regr. + lgbm-class: &id002 + name: LGBM Clas. +dml_parameters: + learners: + - ml_m: *id001 + ml_M: *id002 + ml_t: *id001 + score: + - nuisance_space + - instrument +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/plm/lplr_ate_tune_coverage.csv b/results/plm/lplr_ate_tune_coverage.csv new file mode 100644 index 00000000..8a2c6bf1 --- /dev/null +++ b/results/plm/lplr_ate_tune_coverage.csv @@ -0,0 +1,9 @@ +Learner m,Learner M,Learner t,Score,level,Tuned,Coverage,CI Length,Bias,repetition +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,False,0.91,0.9117258212067718,0.240354871477558,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,True,0.95,0.8692681775643711,0.2054770002796413,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,False,0.98,1.0863883229855305,0.240354871477558,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,True,0.96,1.0357969201737371,0.2054770002796413,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,False,0.91,0.7841573908306078,0.18430486050109982,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,True,0.86,0.7221800622589235,0.1665060542122647,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,False,0.95,0.9343811625885382,0.18430486050109982,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,True,0.93,0.8605306205900738,0.1665060542122647,100 diff --git a/results/plm/lplr_ate_tune_metadata.csv b/results/plm/lplr_ate_tune_metadata.csv new file mode 100644 index 00000000..7b432298 --- /dev/null +++ b/results/plm/lplr_ate_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.12.dev0,LPLRATETuningCoverageSimulation,2025-11-26 17:47,44.12576818863551,3.12.9,scripts/plm/lplr_ate_tune_config.yml diff --git a/scripts/plm/lplr_ate_config.yml b/scripts/plm/lplr_ate_config.yml index 78c930a8..b0a540a8 100644 --- a/scripts/plm/lplr_ate_config.yml +++ b/scripts/plm/lplr_ate_config.yml @@ -7,7 +7,7 @@ simulation_parameters: n_jobs: -2 dgp_parameters: - theta: [0.5] # Treatment effect + alpha: [0.5] # Treatment effect n_obs: [500] # Sample size dim_x: [20] # Number of covariates diff --git a/scripts/plm/lplr_ate_tune.py b/scripts/plm/lplr_ate_tune.py new file mode 100644 index 00000000..127c3de6 --- /dev/null +++ b/scripts/plm/lplr_ate_tune.py @@ -0,0 +1,14 @@ +from montecover.plm import LPLRATETuningCoverageSimulation + +# Create and run simulation with config file +sim = LPLRATETuningCoverageSimulation( + config_file="scripts/plm/lplr_ate_tune_config.yml", + log_level="INFO", + log_file="logs/plm/lplr_ate_tune_sim.log", +) +print("Calling file") +sim.run_simulation() +sim.save_results(output_path="results/plm/", file_prefix="lplr_ate_tune") + +# Save config file for reproducibility +sim.save_config("results/plm/lplr_ate_tune_config.yml") diff --git a/scripts/plm/lplr_ate_tune_config.yml b/scripts/plm/lplr_ate_tune_config.yml new file mode 100644 index 00000000..52cab900 --- /dev/null +++ b/scripts/plm/lplr_ate_tune_config.yml @@ -0,0 +1,31 @@ +# Simulation parameters for LPLR ATE Coverage + +simulation_parameters: + repetitions: 100 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + alpha: [0.5] # Treatment effect + n_obs: [500] # Sample size + dim_x: [20] # Number of covariates + +# Define reusable learner configurations +learner_definitions: + lgbm: &lgbm + name: "LGBM Regr." + + lgbm-class: &lgbm-class + name: "LGBM Clas." + +dml_parameters: + learners: + - ml_m: *lgbm + ml_M: *lgbm-class + ml_t: *lgbm + + score: ["nuisance_space", "instrument"] + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From 454361c3453febe30c5050e55edc01705b793685 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 26 Nov 2025 19:54:33 +0100 Subject: [PATCH 10/51] formatting --- .github/workflows/apo_sim.yml | 2 +- .github/workflows/pliv_sim.yml | 2 +- doc/irm/irm.qmd | 6 +- doc/plm/plr.qmd | 2 +- monte-cover/src/montecover/base.py | 51 +++++++++++---- .../src/montecover/did/did_cs_multi.py | 36 +++++++--- .../src/montecover/did/did_pa_multi.py | 4 +- monte-cover/src/montecover/irm/__init__.py | 2 +- monte-cover/src/montecover/irm/apo.py | 16 +++-- monte-cover/src/montecover/irm/apos.py | 16 +++-- monte-cover/src/montecover/irm/apos_tune.py | 65 ++++++++++++------- monte-cover/src/montecover/irm/cvar.py | 25 +++++-- monte-cover/src/montecover/irm/iivm_late.py | 12 +++- monte-cover/src/montecover/irm/irm_ate.py | 12 +++- .../src/montecover/irm/irm_ate_sensitivity.py | 22 +++++-- .../src/montecover/irm/irm_ate_tune.py | 57 +++++++++------- monte-cover/src/montecover/irm/irm_atte.py | 12 +++- .../montecover/irm/irm_atte_sensitivity.py | 22 +++++-- monte-cover/src/montecover/irm/irm_cate.py | 34 +++++++--- monte-cover/src/montecover/irm/irm_gate.py | 20 ++++-- monte-cover/src/montecover/irm/lpq.py | 40 +++++++++--- monte-cover/src/montecover/irm/pq.py | 25 +++++-- monte-cover/src/montecover/plm/__init__.py | 6 +- monte-cover/src/montecover/plm/lplr_ate.py | 20 +++--- .../src/montecover/plm/lplr_ate_tune.py | 62 +++++++++++------- monte-cover/src/montecover/plm/pliv_late.py | 8 ++- monte-cover/src/montecover/plm/plr_ate.py | 8 ++- .../src/montecover/plm/plr_ate_sensitivity.py | 48 ++++++++++---- .../src/montecover/plm/plr_ate_tune.py | 53 ++++++++------- monte-cover/src/montecover/plm/plr_cate.py | 30 +++++++-- monte-cover/src/montecover/plm/plr_gate.py | 16 +++-- monte-cover/src/montecover/rdd/rdd.py | 52 +++++++++++---- monte-cover/src/montecover/ssm/ssm_mar_ate.py | 12 +++- .../src/montecover/ssm/ssm_nonig_ate.py | 12 +++- monte-cover/src/montecover/utils.py | 15 ++++- 35 files changed, 580 insertions(+), 245 deletions(-) diff --git a/.github/workflows/apo_sim.yml b/.github/workflows/apo_sim.yml index 31ee1cbd..9712005e 100644 --- a/.github/workflows/apo_sim.yml +++ b/.github/workflows/apo_sim.yml @@ -52,7 +52,7 @@ jobs: uses: astral-sh/setup-uv@v5 with: version: "0.7.8" - + - name: Set up Python uses: actions/setup-python@v5 with: diff --git a/.github/workflows/pliv_sim.yml b/.github/workflows/pliv_sim.yml index 22a91bc4..4c1233ed 100644 --- a/.github/workflows/pliv_sim.yml +++ b/.github/workflows/pliv_sim.yml @@ -62,7 +62,7 @@ jobs: cd monte-cover uv venv uv sync - + - name: Install DoubleML from correct branch run: | source monte-cover/.venv/bin/activate diff --git a/doc/irm/irm.qmd b/doc/irm/irm.qmd index db4b53fb..272ae190 100644 --- a/doc/irm/irm.qmd +++ b/doc/irm/irm.qmd @@ -37,7 +37,7 @@ print(metadata_df.T.to_string(header=False)) ::: -### ATE +### ATE ```{python} #| echo: false @@ -264,7 +264,7 @@ print(metadata_df.T.to_string(header=False)) ::: -### ATE +### ATE ```{python} #| echo: false @@ -304,4 +304,4 @@ generate_and_show_styled_table( level_col="level", coverage_highlight_cols=["Coverage"] ) -``` \ No newline at end of file +``` diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd index 23552f37..6c1c366f 100644 --- a/doc/plm/plr.qmd +++ b/doc/plm/plr.qmd @@ -254,4 +254,4 @@ generate_and_show_styled_table( rename_map={"Learner g": "Learner l"}, coverage_highlight_cols=["Coverage"] ) -``` \ No newline at end of file +``` diff --git a/monte-cover/src/montecover/base.py b/monte-cover/src/montecover/base.py index 1695e2e6..6309c68c 100644 --- a/monte-cover/src/montecover/base.py +++ b/monte-cover/src/montecover/base.py @@ -107,7 +107,9 @@ def run_simulation(self, n_jobs=None): rep_end_time = time.time() rep_duration = rep_end_time - rep_start_time - self.logger.info(f"Repetition {i_rep+1} completed in {rep_duration:.2f}s") + self.logger.info( + f"Repetition {i_rep+1} completed in {rep_duration:.2f}s" + ) else: self.logger.info(f"Starting parallel execution with n_jobs={n_jobs}") @@ -138,7 +140,9 @@ def save_results(self, output_path: str = "results", file_prefix: str = ""): "Script": [self.__class__.__name__], "Date": [datetime.now().strftime("%Y-%m-%d %H:%M")], "Total Runtime (minutes)": [self.total_runtime / 60], - "Python Version": [f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"], + "Python Version": [ + f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}" + ], "Config File": [self.config_file], } ) @@ -161,7 +165,14 @@ def save_config(self, output_path: str): self.logger.warning(f"Adding .yaml extension to output path: {output_path}") with open(output_path, "w") as file: - yaml.dump(self.config, file, sort_keys=False, default_flow_style=False, indent=2, allow_unicode=True) + yaml.dump( + self.config, + file, + sort_keys=False, + default_flow_style=False, + indent=2, + allow_unicode=True, + ) self.logger.info(f"Configuration saved to {output_path}") @@ -174,7 +185,9 @@ def _load_config(self, config_path: str) -> Dict[str, Any]: with open(config_path, "r") as file: config = yaml.safe_load(file) else: - raise ValueError(f"Unsupported config file format: {config_path}. Use .yaml or .yml") + raise ValueError( + f"Unsupported config file format: {config_path}. Use .yaml or .yml" + ) return config @@ -198,7 +211,9 @@ def _setup_logging(self, log_level: str, log_file: Optional[str]): # Console handler ch = logging.StreamHandler() ch.setLevel(level) - formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") + formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + ) ch.setFormatter(formatter) self.logger.addHandler(ch) @@ -256,7 +271,9 @@ def _process_repetition(self, i_rep): dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values)) i_param_comb += 1 - comb_results = self._process_parameter_combination(i_rep, i_param_comb, dgp_params, dml_params, dml_data) + comb_results = self._process_parameter_combination( + i_rep, i_param_comb, dgp_params, dml_params, dml_data + ) # Merge results for result_name, result_list in comb_results.items(): @@ -266,11 +283,14 @@ def _process_repetition(self, i_rep): return rep_results - def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_params, dml_data): + def _process_parameter_combination( + self, i_rep, i_param_comb, dgp_params, dml_params, dml_data + ): """Process a single parameter combination.""" # Log parameter combination self.logger.debug( - f"Rep {i_rep+1}, Combo {i_param_comb}/{self.total_combinations}: " f"DGPs {dgp_params}, DML {dml_params}" + f"Rep {i_rep+1}, Combo {i_param_comb}/{self.total_combinations}: " + f"DGPs {dgp_params}, DML {dml_params}" ) param_start_time = time.time() @@ -279,7 +299,9 @@ def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_pa # Log timing param_duration = time.time() - param_start_time - self.logger.debug(f"Parameter combination completed in {param_duration:.2f}s") + self.logger.debug( + f"Parameter combination completed in {param_duration:.2f}s" + ) # Process results if repetition_results is None: @@ -298,7 +320,8 @@ def _process_parameter_combination(self, i_rep, i_param_comb, dgp_params, dml_pa except Exception as e: self.logger.error( - f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, " f"DML parameters {dml_params}: {str(e)}" + f"Error: repetition {i_rep+1}, DGP parameters {dgp_params}, " + f"DML parameters {dml_params}: {str(e)}" ) self.logger.exception("Exception details:") return {} @@ -333,9 +356,13 @@ def _compute_coverage(thetas, oracle_thetas, confint, joint_confint=None): if joint_confint is not None: joint_lower_bound = joint_confint.iloc[:, 0] joint_upper_bound = joint_confint.iloc[:, 1] - joint_coverage_mask = (joint_lower_bound < oracle_thetas) & (oracle_thetas < joint_upper_bound) + joint_coverage_mask = (joint_lower_bound < oracle_thetas) & ( + oracle_thetas < joint_upper_bound + ) result_dict["Uniform Coverage"] = np.all(joint_coverage_mask) - result_dict["Uniform CI Length"] = np.mean(joint_upper_bound - joint_lower_bound) + result_dict["Uniform CI Length"] = np.mean( + joint_upper_bound - joint_lower_bound + ) return result_dict diff --git a/monte-cover/src/montecover/did/did_cs_multi.py b/monte-cover/src/montecover/did/did_cs_multi.py index ea11cd25..a901cdc7 100644 --- a/monte-cover/src/montecover/did/did_cs_multi.py +++ b/monte-cover/src/montecover/did/did_cs_multi.py @@ -35,7 +35,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -54,19 +56,23 @@ def _calculate_oracle_values(self): lambda_t=self.dgp_parameters["lambda_t"][0], ) # does not depend on the DGP type or lambda_t df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"] - self.oracle_values["detailed"] = df_oracle.groupby(["d", "t"])["ite"].mean().reset_index() + self.oracle_values["detailed"] = ( + df_oracle.groupby(["d", "t"])["ite"].mean().reset_index() + ) # Oracle group aggregation df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]] - self.oracle_values["group"] = df_oracle_post_treatment.groupby("d")["ite"].mean() + self.oracle_values["group"] = df_oracle_post_treatment.groupby("d")[ + "ite" + ].mean() # Oracle time aggregation self.oracle_values["time"] = df_oracle_post_treatment.groupby("t")["ite"].mean() # Oracle eventstudy aggregation - df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype("datetime64[M]") - pd.to_datetime( - df_oracle["d"] - ).values.astype("datetime64[M]") + df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype( + "datetime64[M]" + ) - pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]") self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:] def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: @@ -96,7 +102,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: for i, (g, _, t) in enumerate(dml_model.gt_combinations): group_index = self.oracle_values["detailed"]["d"] == g time_index = self.oracle_values["detailed"]["t"] == t - oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index]["ite"].iloc[0] + oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index][ + "ite" + ].iloc[0] result = { "detailed": [], @@ -121,7 +129,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: thetas=agg_obj.aggregated_frameworks.thetas, oracle_thetas=self.oracle_values[aggregation_method].values, confint=agg_obj.aggregated_frameworks.confint(level=level), - joint_confint=agg_obj.aggregated_frameworks.confint(level=level, joint=True), + joint_confint=agg_obj.aggregated_frameworks.confint( + level=level, joint=True + ), ) # add parameters to the result @@ -163,14 +173,20 @@ def summarize_results(self): result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData: """Generate data for the simulation.""" - data = make_did_cs_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"], lambda_t=dgp_params["lambda_t"]) + data = make_did_cs_CS2021( + n_obs=dgp_params["n_obs"], + dgp_type=dgp_params["DGP"], + lambda_t=dgp_params["lambda_t"], + ) dml_data = dml.data.DoubleMLPanelData( data, y_col="y", diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py index eb849347..4a9856b4 100644 --- a/monte-cover/src/montecover/did/did_pa_multi.py +++ b/monte-cover/src/montecover/did/did_pa_multi.py @@ -36,7 +36,9 @@ def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: diff --git a/monte-cover/src/montecover/irm/__init__.py b/monte-cover/src/montecover/irm/__init__.py index c54e9991..e920e730 100644 --- a/monte-cover/src/montecover/irm/__init__.py +++ b/monte-cover/src/montecover/irm/__init__.py @@ -6,8 +6,8 @@ from montecover.irm.cvar import CVARCoverageSimulation from montecover.irm.iivm_late import IIVMLATECoverageSimulation from montecover.irm.irm_ate import IRMATECoverageSimulation -from montecover.irm.irm_ate_tune import IRMATETuningCoverageSimulation from montecover.irm.irm_ate_sensitivity import IRMATESensitivityCoverageSimulation +from montecover.irm.irm_ate_tune import IRMATETuningCoverageSimulation from montecover.irm.irm_atte import IRMATTECoverageSimulation from montecover.irm.irm_atte_sensitivity import IRMATTESensitivityCoverageSimulation from montecover.irm.irm_cate import IRMCATECoverageSimulation diff --git a/monte-cover/src/montecover/irm/apo.py b/monte-cover/src/montecover/irm/apo.py index 19a1c14e..88c84145 100644 --- a/monte-cover/src/montecover/irm/apo.py +++ b/monte-cover/src/montecover/irm/apo.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -62,7 +64,9 @@ def _calculate_oracle_values(self): for i in range(n_levels): ates[i] = apos[i + 1] - apos[0] - self.logger.info(f"Levels and their counts:\n{np.unique(d, return_counts=True)}") + self.logger.info( + f"Levels and their counts:\n{np.unique(d, return_counts=True)}" + ) self.logger.info(f"True APOs: {apos}") self.logger.info(f"True ATEs: {ates}") @@ -70,7 +74,9 @@ def _calculate_oracle_values(self): self.oracle_values["apos"] = apos self.oracle_values["ates"] = ates - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -132,7 +138,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/apos.py b/monte-cover/src/montecover/irm/apos.py index 70d5ce65..8cf885ba 100644 --- a/monte-cover/src/montecover/irm/apos.py +++ b/monte-cover/src/montecover/irm/apos.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -62,7 +64,9 @@ def _calculate_oracle_values(self): for i in range(n_levels): ates[i] = apos[i + 1] - apos[0] - self.logger.info(f"Levels and their counts:\n{np.unique(d, return_counts=True)}") + self.logger.info( + f"Levels and their counts:\n{np.unique(d, return_counts=True)}" + ) self.logger.info(f"True APOs: {apos}") self.logger.info(f"True ATEs: {ates}") @@ -70,7 +74,9 @@ def _calculate_oracle_values(self): self.oracle_values["apos"] = apos self.oracle_values["ates"] = ates - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -144,7 +150,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/apos_tune.py b/monte-cover/src/montecover/irm/apos_tune.py index 447b7a2b..ece2ae38 100644 --- a/monte-cover/src/montecover/irm/apos_tune.py +++ b/monte-cover/src/montecover/irm/apos_tune.py @@ -1,8 +1,8 @@ from typing import Any, Dict, Optional -import optuna import doubleml as dml import numpy as np +import optuna import pandas as pd from doubleml.irm.datasets import make_irm_data_discrete_treatments @@ -34,40 +34,47 @@ def __init__( # parameter space for the outcome regression tuning def ml_g_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 200, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 50, step=5), - 'max_depth': 5, - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-3, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-3, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 50, step=5 + ), + "max_depth": 5, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), } # parameter space for the propensity score tuning def ml_m_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 200, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 50, step=5), - 'max_depth': 5, - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-3, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-3, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 50, step=5 + ), + "max_depth": 5, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), } - self._param_space = { - 'ml_g': ml_g_params, - 'ml_m': ml_m_params - } + self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} self._optuna_settings = { - 'n_trials': 200, - 'show_progress_bar': False, - 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + "n_trials": 200, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -97,7 +104,9 @@ def _calculate_oracle_values(self): for i in range(n_levels): ates[i] = apos[i + 1] - apos[0] - self.logger.info(f"Levels and their counts:\n{np.unique(d, return_counts=True)}") + self.logger.info( + f"Levels and their counts:\n{np.unique(d, return_counts=True)}" + ) self.logger.info(f"True APOs: {apos}") self.logger.info(f"True ATEs: {ates}") @@ -105,7 +114,9 @@ def _calculate_oracle_values(self): self.oracle_values["apos"] = apos self.oracle_values["ates"] = ates - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -156,7 +167,9 @@ def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) thetas=causal_contrast_model.thetas, oracle_thetas=self.oracle_values["ates"], confint=causal_contrast_model.confint(level=level), - joint_confint=causal_contrast_model.confint(level=level, joint=True), + joint_confint=causal_contrast_model.confint( + level=level, joint=True + ), ) # add parameters to the result @@ -192,7 +205,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/cvar.py b/monte-cover/src/montecover/irm/cvar.py index 19180c09..15028967 100644 --- a/monte-cover/src/montecover/irm/cvar.py +++ b/monte-cover/src/montecover/irm/cvar.py @@ -10,7 +10,13 @@ # define loc-scale model def f_loc(D, X): - loc = 0.5 * D + 2 * D * X[:, 4] + 2.0 * (X[:, 1] > 0.1) - 1.7 * (X[:, 0] * X[:, 2] > 0) - 3 * X[:, 3] + loc = ( + 0.5 * D + + 2 * D * X[:, 4] + + 2.0 * (X[:, 1] > 0.1) + - 1.7 * (X[:, 0] * X[:, 2] > 0) + - 3 * X[:, 3] + ) return loc @@ -51,7 +57,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -87,7 +95,9 @@ def _calculate_oracle_values(self): self.logger.info(f"Oracle values: {self.oracle_values}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -189,7 +199,9 @@ def summarize_results(self): # Aggregate results for Y0 and Y1 for result_name in ["Y0_coverage", "Y1_coverage"]: df = self.results[result_name] - result_summary[result_name] = df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") uniform_aggregation_dict = { @@ -201,7 +213,10 @@ def summarize_results(self): "repetition": "count", } result_summary["effect_coverage"] = ( - self.results["effect_coverage"].groupby(groupby_cols).agg(uniform_aggregation_dict).reset_index() + self.results["effect_coverage"] + .groupby(groupby_cols) + .agg(uniform_aggregation_dict) + .reset_index() ) self.logger.debug("Summarized effect_coverage results") diff --git a/monte-cover/src/montecover/irm/iivm_late.py b/monte-cover/src/montecover/irm/iivm_late.py index 10f45443..0cd53a47 100644 --- a/monte-cover/src/montecover/irm/iivm_late.py +++ b/monte-cover/src/montecover/irm/iivm_late.py @@ -30,7 +30,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m", "ml_r"] for learner in self.dml_parameters["learners"]: @@ -44,7 +46,9 @@ def _calculate_oracle_values(self): self.oracle_values = dict() self.oracle_values["theta"] = self.dgp_parameters["theta"] - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -104,7 +108,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_ate.py b/monte-cover/src/montecover/irm/irm_ate.py index 7e149ef8..b541ac41 100644 --- a/monte-cover/src/montecover/irm/irm_ate.py +++ b/monte-cover/src/montecover/irm/irm_ate.py @@ -30,7 +30,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -44,7 +46,9 @@ def _calculate_oracle_values(self): self.oracle_values = dict() self.oracle_values["theta"] = self.dgp_parameters["theta"] - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -101,7 +105,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_ate_sensitivity.py b/monte-cover/src/montecover/irm/irm_ate_sensitivity.py index c95f9ef0..c31c700a 100644 --- a/monte-cover/src/montecover/irm/irm_ate_sensitivity.py +++ b/monte-cover/src/montecover/irm/irm_ate_sensitivity.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -101,12 +103,18 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: null_hypothesis=theta, ) sensitivity_results = { - "Coverage (Lower)": theta >= dml_model.sensitivity_params["ci"]["lower"][0], - "Coverage (Upper)": theta <= dml_model.sensitivity_params["ci"]["upper"][0], + "Coverage (Lower)": theta + >= dml_model.sensitivity_params["ci"]["lower"][0], + "Coverage (Upper)": theta + <= dml_model.sensitivity_params["ci"]["upper"][0], "RV": dml_model.sensitivity_params["rv"][0], "RVa": dml_model.sensitivity_params["rva"][0], - "Bias (Lower)": abs(theta - dml_model.sensitivity_params["theta"]["lower"][0]), - "Bias (Upper)": abs(theta - dml_model.sensitivity_params["theta"]["upper"][0]), + "Bias (Lower)": abs( + theta - dml_model.sensitivity_params["theta"]["lower"][0] + ), + "Bias (Upper)": abs( + theta - dml_model.sensitivity_params["theta"]["upper"][0] + ), } # add sensitivity results to the level result coverage level_result["coverage"].update(sensitivity_results) @@ -147,7 +155,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_ate_tune.py b/monte-cover/src/montecover/irm/irm_ate_tune.py index 19d52b24..4ebb947a 100644 --- a/monte-cover/src/montecover/irm/irm_ate_tune.py +++ b/monte-cover/src/montecover/irm/irm_ate_tune.py @@ -1,7 +1,7 @@ from typing import Any, Dict, Optional -import optuna import doubleml as dml +import optuna from doubleml.irm.datasets import make_irm_data from montecover.base import BaseSimulation @@ -32,40 +32,47 @@ def __init__( # parameter space for the outcome regression tuning def ml_g_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), - 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 100, step=5 + ), + "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } # parameter space for the propensity score tuning def ml_m_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), - 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 100, step=5 + ), + "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } - self._param_space = { - 'ml_g': ml_g_params, - 'ml_m': ml_m_params - } + self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} self._optuna_settings = { - 'n_trials': 500, - 'show_progress_bar': False, - 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + "n_trials": 500, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -79,7 +86,9 @@ def _calculate_oracle_values(self): self.oracle_values = dict() self.oracle_values["theta"] = self.dgp_parameters["theta"] - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -149,7 +158,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_atte.py b/monte-cover/src/montecover/irm/irm_atte.py index cb25a894..64eb4d8a 100644 --- a/monte-cover/src/montecover/irm/irm_atte.py +++ b/monte-cover/src/montecover/irm/irm_atte.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -86,7 +88,9 @@ def _calculate_oracle_values(self): self.oracle_values["theta"] = np.mean(y1[d == 1] - y0[d == 1]) self.logger.info(f"Oracle ATTE value: {self.oracle_values['theta']}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -144,7 +148,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_atte_sensitivity.py b/monte-cover/src/montecover/irm/irm_atte_sensitivity.py index ef054950..92a66364 100644 --- a/monte-cover/src/montecover/irm/irm_atte_sensitivity.py +++ b/monte-cover/src/montecover/irm/irm_atte_sensitivity.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -101,12 +103,18 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: null_hypothesis=theta, ) sensitivity_results = { - "Coverage (Lower)": theta >= dml_model.sensitivity_params["ci"]["lower"][0], - "Coverage (Upper)": theta <= dml_model.sensitivity_params["ci"]["upper"][0], + "Coverage (Lower)": theta + >= dml_model.sensitivity_params["ci"]["lower"][0], + "Coverage (Upper)": theta + <= dml_model.sensitivity_params["ci"]["upper"][0], "RV": dml_model.sensitivity_params["rv"][0], "RVa": dml_model.sensitivity_params["rva"][0], - "Bias (Lower)": abs(theta - dml_model.sensitivity_params["theta"]["lower"][0]), - "Bias (Upper)": abs(theta - dml_model.sensitivity_params["theta"]["upper"][0]), + "Bias (Lower)": abs( + theta - dml_model.sensitivity_params["theta"]["lower"][0] + ), + "Bias (Upper)": abs( + theta - dml_model.sensitivity_params["theta"]["upper"][0] + ), } # add sensitivity results to the level result coverage level_result["coverage"].update(sensitivity_results) @@ -147,7 +155,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_cate.py b/monte-cover/src/montecover/irm/irm_cate.py index cb0f2264..45fe8df6 100644 --- a/monte-cover/src/montecover/irm/irm_cate.py +++ b/monte-cover/src/montecover/irm/irm_cate.py @@ -34,7 +34,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -54,14 +56,18 @@ def _calculate_oracle_values(self): self.logger.info("Calculating oracle values") - design_matrix_oracle = patsy.dmatrix("bs(x, df=5, degree=2)", {"x": data_oracle["data"]["X_0"]}) + design_matrix_oracle = patsy.dmatrix( + "bs(x, df=5, degree=2)", {"x": data_oracle["data"]["X_0"]} + ) spline_basis_oracle = pd.DataFrame(design_matrix_oracle) oracle_model = LinearRegression() oracle_model.fit(spline_basis_oracle, data_oracle["effects"]) # evaluate on grid grid = {"x": np.linspace(0.1, 0.9, 100)} - spline_grid_oracle = pd.DataFrame(patsy.build_design_matrices([design_matrix_oracle.design_info], grid)[0]) + spline_grid_oracle = pd.DataFrame( + patsy.build_design_matrices([design_matrix_oracle.design_info], grid)[0] + ) oracle_cates = oracle_model.predict(spline_grid_oracle) self.oracle_values = dict() @@ -70,7 +76,9 @@ def _calculate_oracle_values(self): self.logger.info(f"Oracle values: {self.oracle_values}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -86,12 +94,18 @@ def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) dml_model.fit() # cate - design_matrix = patsy.dmatrix("bs(x, df=5, degree=2)", {"x": dml_data.data["X_0"]}) + design_matrix = patsy.dmatrix( + "bs(x, df=5, degree=2)", {"x": dml_data.data["X_0"]} + ) spline_basis = pd.DataFrame(design_matrix) cate_model = dml_model.cate(basis=spline_basis) # evaluation spline basis - spline_grid = pd.DataFrame(patsy.build_design_matrices([design_matrix.design_info], self.oracle_values["grid"])[0]) + spline_grid = pd.DataFrame( + patsy.build_design_matrices( + [design_matrix.design_info], self.oracle_values["grid"] + )[0] + ) result = { "coverage": [], @@ -100,7 +114,9 @@ def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) level_result = dict() confint = cate_model.confint(basis=spline_grid, level=level) effects = confint["effect"] - uniform_confint = cate_model.confint(basis=spline_grid, level=0.95, joint=True, n_rep_boot=2000) + uniform_confint = cate_model.confint( + basis=spline_grid, level=0.95, joint=True, n_rep_boot=2000 + ) level_result["coverage"] = self._compute_coverage( thetas=effects, oracle_thetas=self.oracle_values["cates"], @@ -140,7 +156,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/irm_gate.py b/monte-cover/src/montecover/irm/irm_gate.py index 469cdbf9..8fa99195 100644 --- a/monte-cover/src/montecover/irm/irm_gate.py +++ b/monte-cover/src/montecover/irm/irm_gate.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -66,14 +68,18 @@ def _calculate_oracle_values(self): self.logger.info("Calculating oracle values") groups = self._generate_groups(data_oracle["data"]) - oracle_gates = [data_oracle["effects"][groups[group]].mean() for group in groups.columns] + oracle_gates = [ + data_oracle["effects"][groups[group]].mean() for group in groups.columns + ] self.oracle_values = dict() self.oracle_values["gates"] = oracle_gates self.logger.info(f"Oracle values: {self.oracle_values}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -99,7 +105,9 @@ def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) level_result = dict() confint = gate_model.confint(level=level) effects = confint["effect"] - uniform_confint = gate_model.confint(level=0.95, joint=True, n_rep_boot=2000) + uniform_confint = gate_model.confint( + level=0.95, joint=True, n_rep_boot=2000 + ) level_result["coverage"] = self._compute_coverage( thetas=effects, oracle_thetas=self.oracle_values["gates"], @@ -139,7 +147,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/irm/lpq.py b/monte-cover/src/montecover/irm/lpq.py index 86b66f3d..979e902e 100644 --- a/monte-cover/src/montecover/irm/lpq.py +++ b/monte-cover/src/montecover/irm/lpq.py @@ -10,7 +10,14 @@ # define loc-scale model def f_loc(D, X, X_conf): - loc = 0.5 * D + 2 * D * X[:, 4] + 2.0 * (X[:, 1] > 0.1) - 1.7 * (X[:, 0] * X[:, 2] > 0) - 3 * X[:, 3] - 2 * X_conf[:, 0] + loc = ( + 0.5 * D + + 2 * D * X[:, 4] + + 2.0 * (X[:, 1] > 0.1) + - 1.7 * (X[:, 0] * X[:, 2] > 0) + - 3 * X[:, 3] + - 2 * X_conf[:, 0] + ) return loc @@ -60,7 +67,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -88,11 +97,19 @@ def _calculate_oracle_values(self): n_compliers = compliers.sum() Y1 = ( f_loc(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) - + f_scale(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) * epsilon_true[compliers] + + f_scale( + np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :] + ) + * epsilon_true[compliers] ) Y0 = ( - f_loc(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) - + f_scale(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) * epsilon_true[compliers] + f_loc( + np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :] + ) + + f_scale( + np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :] + ) + * epsilon_true[compliers] ) Y0_quant = np.quantile(Y0, q=tau_vec) @@ -106,7 +123,9 @@ def _calculate_oracle_values(self): self.logger.info(f"Oracle values: {self.oracle_values}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -208,7 +227,9 @@ def summarize_results(self): # Aggregate results for Y0 and Y1 for result_name in ["Y0_coverage", "Y1_coverage"]: df = self.results[result_name] - result_summary[result_name] = df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") uniform_aggregation_dict = { @@ -220,7 +241,10 @@ def summarize_results(self): "repetition": "count", } result_summary["effect_coverage"] = ( - self.results["effect_coverage"].groupby(groupby_cols).agg(uniform_aggregation_dict).reset_index() + self.results["effect_coverage"] + .groupby(groupby_cols) + .agg(uniform_aggregation_dict) + .reset_index() ) self.logger.debug("Summarized effect_coverage results") diff --git a/monte-cover/src/montecover/irm/pq.py b/monte-cover/src/montecover/irm/pq.py index f935dc33..219d5b1f 100644 --- a/monte-cover/src/montecover/irm/pq.py +++ b/monte-cover/src/montecover/irm/pq.py @@ -10,7 +10,13 @@ # define loc-scale model def f_loc(D, X): - loc = 0.5 * D + 2 * D * X[:, 4] + 2.0 * (X[:, 1] > 0.1) - 1.7 * (X[:, 0] * X[:, 2] > 0) - 3 * X[:, 3] + loc = ( + 0.5 * D + + 2 * D * X[:, 4] + + 2.0 * (X[:, 1] > 0.1) + - 1.7 * (X[:, 0] * X[:, 2] > 0) + - 3 * X[:, 3] + ) return loc @@ -51,7 +57,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -85,7 +93,9 @@ def _calculate_oracle_values(self): self.logger.info(f"Oracle values: {self.oracle_values}") - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -187,7 +197,9 @@ def summarize_results(self): # Aggregate results for Y0 and Y1 for result_name in ["Y0_coverage", "Y1_coverage"]: df = self.results[result_name] - result_summary[result_name] = df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") uniform_aggregation_dict = { @@ -199,7 +211,10 @@ def summarize_results(self): "repetition": "count", } result_summary["effect_coverage"] = ( - self.results["effect_coverage"].groupby(groupby_cols).agg(uniform_aggregation_dict).reset_index() + self.results["effect_coverage"] + .groupby(groupby_cols) + .agg(uniform_aggregation_dict) + .reset_index() ) self.logger.debug("Summarized effect_coverage results") diff --git a/monte-cover/src/montecover/plm/__init__.py b/monte-cover/src/montecover/plm/__init__.py index 4aa935ba..0edaedc7 100644 --- a/monte-cover/src/montecover/plm/__init__.py +++ b/monte-cover/src/montecover/plm/__init__.py @@ -1,13 +1,13 @@ """Monte Carlo coverage simulations for PLM.""" +from montecover.plm.lplr_ate import LPLRATECoverageSimulation +from montecover.plm.lplr_ate_tune import LPLRATETuningCoverageSimulation from montecover.plm.pliv_late import PLIVLATECoverageSimulation from montecover.plm.plr_ate import PLRATECoverageSimulation -from montecover.plm.plr_ate_tune import PLRATETuningCoverageSimulation from montecover.plm.plr_ate_sensitivity import PLRATESensitivityCoverageSimulation +from montecover.plm.plr_ate_tune import PLRATETuningCoverageSimulation from montecover.plm.plr_cate import PLRCATECoverageSimulation from montecover.plm.plr_gate import PLRGATECoverageSimulation -from montecover.plm.lplr_ate import LPLRATECoverageSimulation -from montecover.plm.lplr_ate_tune import LPLRATETuningCoverageSimulation __all__ = [ "PLRATECoverageSimulation", diff --git a/monte-cover/src/montecover/plm/lplr_ate.py b/monte-cover/src/montecover/plm/lplr_ate.py index 2d62ee26..6fec3280 100644 --- a/monte-cover/src/montecover/plm/lplr_ate.py +++ b/monte-cover/src/montecover/plm/lplr_ate.py @@ -11,12 +11,12 @@ class LPLRATECoverageSimulation(BaseSimulation): """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" def __init__( - self, - config_file: str, - suppress_warnings: bool = True, - log_level: str = "INFO", - log_file: Optional[str] = None, - use_failed_scores: bool = False, + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + use_failed_scores: bool = False, ): super().__init__( config_file=config_file, @@ -33,7 +33,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_m", "ml_M", "ml_t"] for learner in self.dml_parameters["learners"]: @@ -116,7 +118,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/lplr_ate_tune.py b/monte-cover/src/montecover/plm/lplr_ate_tune.py index 227339e6..994f7e35 100644 --- a/monte-cover/src/montecover/plm/lplr_ate_tune.py +++ b/monte-cover/src/montecover/plm/lplr_ate_tune.py @@ -1,7 +1,7 @@ from typing import Any, Dict, Optional -import optuna import doubleml as dml +import optuna from doubleml.plm.datasets import make_lplr_LZZ2020 from montecover.base import BaseSimulation @@ -12,12 +12,12 @@ class LPLRATETuningCoverageSimulation(BaseSimulation): """Simulation class for coverage properties of DoubleMLPLR for ATE estimation.""" def __init__( - self, - config_file: str, - suppress_warnings: bool = True, - log_level: str = "INFO", - log_file: Optional[str] = None, - use_failed_scores: bool = False, + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + use_failed_scores: bool = False, ): super().__init__( config_file=config_file, @@ -33,31 +33,37 @@ def __init__( # for simplicity, we use the same parameter space for all learners def ml_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), - 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 100, step=5 + ), + "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } self._param_space = { - 'ml_M': ml_params, - 'ml_t': ml_params, - 'ml_m': ml_params, - 'ml_a': ml_params, + "ml_M": ml_params, + "ml_t": ml_params, + "ml_m": ml_params, + "ml_a": ml_params, } self._optuna_settings = { - 'n_trials': 200, - 'show_progress_bar': False, - 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + "n_trials": 200, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_m", "ml_M", "ml_t"] for learner in self.dml_parameters["learners"]: @@ -87,7 +93,6 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "ml_t": ml_t, "score": score, "error_on_convergence_failure": not self._use_failed_scores, - } # Model dml_model = dml.DoubleMLLPLR(**model_inputs) @@ -139,7 +144,14 @@ def summarize_results(self): self.logger.info("Summarizing simulation results") # Group by parameter combinations - groupby_cols = ["Learner m", "Learner M", "Learner t", "Score", "level", "Tuned"] + groupby_cols = [ + "Learner m", + "Learner M", + "Learner t", + "Score", + "level", + "Tuned", + ] aggregation_dict = { "Coverage": "mean", "CI Length": "mean", @@ -150,7 +162,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/pliv_late.py b/monte-cover/src/montecover/plm/pliv_late.py index c7d86254..056b0eaa 100644 --- a/monte-cover/src/montecover/plm/pliv_late.py +++ b/monte-cover/src/montecover/plm/pliv_late.py @@ -30,7 +30,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m", "ml_r"] for learner in self.dml_parameters["learners"]: @@ -109,7 +111,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/plr_ate.py b/monte-cover/src/montecover/plm/plr_ate.py index 2c8e0240..0f192141 100644 --- a/monte-cover/src/montecover/plm/plr_ate.py +++ b/monte-cover/src/montecover/plm/plr_ate.py @@ -30,7 +30,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -105,7 +107,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/plr_ate_sensitivity.py b/monte-cover/src/montecover/plm/plr_ate_sensitivity.py index ff94e7f7..ee2fb761 100644 --- a/monte-cover/src/montecover/plm/plr_ate_sensitivity.py +++ b/monte-cover/src/montecover/plm/plr_ate_sensitivity.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -48,21 +50,31 @@ def _calculate_oracle_values(self): cf_d = 0.1 np.random.seed(42) - dgp_dict = make_confounded_plr_data(n_obs=int(1e6), cf_y=cf_y, cf_d=cf_d, theta=self.dgp_parameters["theta"]) - oracle_dict = dgp_dict["oracle_values"] - cf_y_test = np.mean(np.square(oracle_dict["g_long"] - oracle_dict["g_short"])) / np.mean( - np.square(dgp_dict["y"] - oracle_dict["g_short"]) + dgp_dict = make_confounded_plr_data( + n_obs=int(1e6), cf_y=cf_y, cf_d=cf_d, theta=self.dgp_parameters["theta"] ) + oracle_dict = dgp_dict["oracle_values"] + cf_y_test = np.mean( + np.square(oracle_dict["g_long"] - oracle_dict["g_short"]) + ) / np.mean(np.square(dgp_dict["y"] - oracle_dict["g_short"])) self.logger.info(f"Input cf_y:{cf_y} \nCalculated cf_y: {round(cf_y_test, 5)}") - rr_long = (dgp_dict["d"] - oracle_dict["m_long"]) / np.mean(np.square(dgp_dict["d"] - oracle_dict["m_long"])) - rr_short = (dgp_dict["d"] - oracle_dict["m_short"]) / np.mean(np.square(dgp_dict["d"] - oracle_dict["m_short"])) - C2_D = (np.mean(np.square(rr_long)) - np.mean(np.square(rr_short))) / np.mean(np.square(rr_short)) + rr_long = (dgp_dict["d"] - oracle_dict["m_long"]) / np.mean( + np.square(dgp_dict["d"] - oracle_dict["m_long"]) + ) + rr_short = (dgp_dict["d"] - oracle_dict["m_short"]) / np.mean( + np.square(dgp_dict["d"] - oracle_dict["m_short"]) + ) + C2_D = (np.mean(np.square(rr_long)) - np.mean(np.square(rr_short))) / np.mean( + np.square(rr_short) + ) cf_d_test = C2_D / (1 + C2_D) self.logger.info(f"Input cf_d:{cf_d}\nCalculated cf_d: {round(cf_d_test, 5)}") # compute the value for rho - rho = np.corrcoef((oracle_dict["g_long"] - oracle_dict["g_short"]), (rr_long - rr_short))[0, 1] + rho = np.corrcoef( + (oracle_dict["g_long"] - oracle_dict["g_short"]), (rr_long - rr_short) + )[0, 1] self.logger.info(f"Correlation rho: {round(rho, 5)}") self.oracle_values = { @@ -112,12 +124,18 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: null_hypothesis=theta, ) sensitivity_results = { - "Coverage (Lower)": theta >= dml_model.sensitivity_params["ci"]["lower"][0], - "Coverage (Upper)": theta <= dml_model.sensitivity_params["ci"]["upper"][0], + "Coverage (Lower)": theta + >= dml_model.sensitivity_params["ci"]["lower"][0], + "Coverage (Upper)": theta + <= dml_model.sensitivity_params["ci"]["upper"][0], "RV": dml_model.sensitivity_params["rv"][0], "RVa": dml_model.sensitivity_params["rva"][0], - "Bias (Lower)": abs(theta - dml_model.sensitivity_params["theta"]["lower"][0]), - "Bias (Upper)": abs(theta - dml_model.sensitivity_params["theta"]["upper"][0]), + "Bias (Lower)": abs( + theta - dml_model.sensitivity_params["theta"]["lower"][0] + ), + "Bias (Upper)": abs( + theta - dml_model.sensitivity_params["theta"]["upper"][0] + ), } # add sensitivity results to the level result coverage level_result["coverage"].update(sensitivity_results) @@ -159,7 +177,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/plr_ate_tune.py b/monte-cover/src/montecover/plm/plr_ate_tune.py index ecf19a1f..1e8b268c 100644 --- a/monte-cover/src/montecover/plm/plr_ate_tune.py +++ b/monte-cover/src/montecover/plm/plr_ate_tune.py @@ -1,7 +1,7 @@ from typing import Any, Dict, Optional -import optuna import doubleml as dml +import optuna from doubleml.plm.datasets import make_plr_CCDDHNR2018 from montecover.base import BaseSimulation @@ -32,40 +32,47 @@ def __init__( # parameter space for the outcome regression tuning def ml_l_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), - 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 100, step=5 + ), + "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } # parameter space for the propensity score tuning def ml_m_params(trial): return { - 'n_estimators': trial.suggest_int('n_estimators', 100, 500, step=50), - 'learning_rate': trial.suggest_float('learning_rate', 1e-3, 0.1, log=True), - 'min_child_samples': trial.suggest_int('min_child_samples', 20, 100, step=5), - 'max_depth': trial.suggest_int('max_depth', 3, 10, step=1), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-8, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-8, 10.0, log=True), + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 100, step=5 + ), + "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } - self._param_space = { - 'ml_l': ml_l_params, - 'ml_m': ml_m_params - } + self._param_space = {"ml_l": ml_l_params, "ml_m": ml_m_params} self._optuna_settings = { - 'n_trials': 500, - 'show_progress_bar': False, - 'verbosity': optuna.logging.WARNING, # Suppress Optuna logs + "n_trials": 500, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -155,7 +162,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/plr_cate.py b/monte-cover/src/montecover/plm/plr_cate.py index cab396a7..0d4d6f83 100644 --- a/monte-cover/src/montecover/plm/plr_cate.py +++ b/monte-cover/src/montecover/plm/plr_cate.py @@ -34,7 +34,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -54,14 +56,18 @@ def _calculate_oracle_values(self): self.logger.info("Calculating oracle values") - design_matrix_oracle = patsy.dmatrix("bs(x, df=5, degree=2)", {"x": data_oracle["data"]["X_0"]}) + design_matrix_oracle = patsy.dmatrix( + "bs(x, df=5, degree=2)", {"x": data_oracle["data"]["X_0"]} + ) spline_basis_oracle = pd.DataFrame(design_matrix_oracle) oracle_model = LinearRegression() oracle_model.fit(spline_basis_oracle, data_oracle["effects"]) # evaluate on grid grid = {"x": np.linspace(0.1, 0.9, 100)} - spline_grid_oracle = pd.DataFrame(patsy.build_design_matrices([design_matrix_oracle.design_info], grid)[0]) + spline_grid_oracle = pd.DataFrame( + patsy.build_design_matrices([design_matrix_oracle.design_info], grid)[0] + ) oracle_cates = oracle_model.predict(spline_grid_oracle) self.oracle_values = dict() @@ -87,12 +93,18 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: dml_model.fit() # cate - design_matrix = patsy.dmatrix("bs(x, df=5, degree=2)", {"x": dml_data.data["X_0"]}) + design_matrix = patsy.dmatrix( + "bs(x, df=5, degree=2)", {"x": dml_data.data["X_0"]} + ) spline_basis = pd.DataFrame(design_matrix) cate_model = dml_model.cate(basis=spline_basis) # evaluation spline basis - spline_grid = pd.DataFrame(patsy.build_design_matrices([design_matrix.design_info], self.oracle_values["grid"])[0]) + spline_grid = pd.DataFrame( + patsy.build_design_matrices( + [design_matrix.design_info], self.oracle_values["grid"] + )[0] + ) result = { "coverage": [], @@ -101,7 +113,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: level_result = dict() confint = cate_model.confint(basis=spline_grid, level=level) effects = confint["effect"] - uniform_confint = cate_model.confint(basis=spline_grid, level=0.95, joint=True, n_rep_boot=2000) + uniform_confint = cate_model.confint( + basis=spline_grid, level=0.95, joint=True, n_rep_boot=2000 + ) level_result["coverage"] = self._compute_coverage( thetas=effects, oracle_thetas=self.oracle_values["cates"], @@ -142,7 +156,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/plm/plr_gate.py b/monte-cover/src/montecover/plm/plr_gate.py index dda52d41..7cb12135 100644 --- a/monte-cover/src/montecover/plm/plr_gate.py +++ b/monte-cover/src/montecover/plm/plr_gate.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m"] for learner in self.dml_parameters["learners"]: @@ -66,7 +68,9 @@ def _calculate_oracle_values(self): self.logger.info("Calculating oracle values") groups = self._generate_groups(data_oracle["data"]) - oracle_gates = [data_oracle["effects"][groups[group]].mean() for group in groups.columns] + oracle_gates = [ + data_oracle["effects"][groups[group]].mean() for group in groups.columns + ] self.oracle_values = dict() self.oracle_values["gates"] = oracle_gates @@ -100,7 +104,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: level_result = dict() confint = gate_model.confint(level=level) effects = confint["effect"] - uniform_confint = gate_model.confint(level=0.95, joint=True, n_rep_boot=2000) + uniform_confint = gate_model.confint( + level=0.95, joint=True, n_rep_boot=2000 + ) level_result["coverage"] = self._compute_coverage( thetas=effects, oracle_thetas=self.oracle_values["gates"], @@ -141,7 +147,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/rdd/rdd.py b/monte-cover/src/montecover/rdd/rdd.py index c130f0d6..95c9f90f 100644 --- a/monte-cover/src/montecover/rdd/rdd.py +++ b/monte-cover/src/montecover/rdd/rdd.py @@ -40,7 +40,9 @@ def _process_config_parameters(self): """Process simulation-specific parameters from config.""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g"] for learner in self.dml_parameters["learners"]: @@ -51,7 +53,9 @@ def _calculate_oracle_values(self): """Calculate oracle values for the simulation.""" self.logger.info("Calculating oracle values") - data_oracle = make_simple_rdd_data(n_obs=int(1e6), fuzzy=self.fuzzy, cutoff=self.cutoff) + data_oracle = make_simple_rdd_data( + n_obs=int(1e6), fuzzy=self.fuzzy, cutoff=self.cutoff + ) # get oracle value score = data_oracle["score"] ite = data_oracle["oracle_values"]["Y1"] - data_oracle["oracle_values"]["Y0"] @@ -59,7 +63,12 @@ def _calculate_oracle_values(self): # subset score and ite for faster computation score_subset = (score >= (self.cutoff - 0.02)) & (score <= (self.cutoff + 0.02)) self.logger.info(f"Oracle score subset size: {np.sum(score_subset)}") - kernel_reg = KernelReg(endog=ite[score_subset], exog=score[score_subset], var_type="c", reg_type="ll") + kernel_reg = KernelReg( + endog=ite[score_subset], + exog=score[score_subset], + var_type="c", + reg_type="ll", + ) effect_at_cutoff, _ = kernel_reg.fit(np.array([self.cutoff])) oracle_effect = effect_at_cutoff[0] @@ -83,23 +92,31 @@ def _process_repetition(self, i_rep): dml_data = self._generate_dml_data(dgp_params) # --- Run rdrobust benchmark --- - self.logger.debug(f"Rep {i_rep+1}: Running rdrobust benchmark for DGP {dgp_params}") + self.logger.debug( + f"Rep {i_rep+1}: Running rdrobust benchmark for DGP {dgp_params}" + ) param_start_time_rd_benchmark = time.time() # Call the dedicated benchmark function # Pass dml_data, current dgp_params, and repetition index - benchmark_result_list = self._rdrobust_benchmark(dml_data, dgp_params, i_rep) + benchmark_result_list = self._rdrobust_benchmark( + dml_data, dgp_params, i_rep + ) if benchmark_result_list: rep_results["coverage"].extend(benchmark_result_list) param_duration_rd_benchmark = time.time() - param_start_time_rd_benchmark - self.logger.debug(f"rdrobust benchmark for DGP {dgp_params} completed in {param_duration_rd_benchmark:.2f}s") + self.logger.debug( + f"rdrobust benchmark for DGP {dgp_params} completed in {param_duration_rd_benchmark:.2f}s" + ) for dml_param_values in product(*self.dml_parameters.values()): dml_params = dict(zip(self.dml_parameters.keys(), dml_param_values)) i_param_comb += 1 - comb_results = self._process_parameter_combination(i_rep, i_param_comb, dgp_params, dml_params, dml_data) + comb_results = self._process_parameter_combination( + i_rep, i_param_comb, dgp_params, dml_params, dml_data + ) rep_results["coverage"].extend(comb_results["coverage"]) return rep_results @@ -116,14 +133,20 @@ def _rdrobust_benchmark(self, dml_data, dml_params, i_rep): for level in self.confidence_parameters["level"]: if self.fuzzy: D = dml_data.data[dml_data.d_cols] - rd_model = rdrobust(y=Y, x=score, fuzzy=D, covs=Z, c=self.cutoff, level=level * 100) + rd_model = rdrobust( + y=Y, x=score, fuzzy=D, covs=Z, c=self.cutoff, level=level * 100 + ) else: - rd_model = rdrobust(y=Y, x=score, covs=Z, c=self.cutoff, level=level * 100) + rd_model = rdrobust( + y=Y, x=score, covs=Z, c=self.cutoff, level=level * 100 + ) coef_rd = rd_model.coef.loc["Robust", "Coeff"] ci_lower_rd = rd_model.ci.loc["Robust", "CI Lower"] ci_upper_rd = rd_model.ci.loc["Robust", "CI Upper"] - confint_for_compute = pd.DataFrame({"lower": [ci_lower_rd], "upper": [ci_upper_rd]}) + confint_for_compute = pd.DataFrame( + {"lower": [ci_lower_rd], "upper": [ci_upper_rd]} + ) theta_for_compute = np.array([coef_rd]) coverage_metrics = self._compute_coverage( @@ -217,7 +240,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary @@ -232,7 +257,10 @@ def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData: x_cols = ["x" + str(i) for i in range(data["X"].shape[1])] columns = ["y", "d", "score"] + x_cols - df = pd.DataFrame(np.column_stack((data["Y"], data["D"], data["score"], data["X"])), columns=columns) + df = pd.DataFrame( + np.column_stack((data["Y"], data["D"], data["score"], data["X"])), + columns=columns, + ) dml_data = dml.data.DoubleMLRDDData( data=df, diff --git a/monte-cover/src/montecover/ssm/ssm_mar_ate.py b/monte-cover/src/montecover/ssm/ssm_mar_ate.py index 5bd8972f..fe6dc0b8 100644 --- a/monte-cover/src/montecover/ssm/ssm_mar_ate.py +++ b/monte-cover/src/montecover/ssm/ssm_mar_ate.py @@ -30,7 +30,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m", "ml_pi"] for learner in self.dml_parameters["learners"]: @@ -44,7 +46,9 @@ def _calculate_oracle_values(self): self.oracle_values = dict() self.oracle_values["theta"] = self.dgp_parameters["theta"] - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -105,7 +109,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/ssm/ssm_nonig_ate.py b/monte-cover/src/montecover/ssm/ssm_nonig_ate.py index dfb75605..62c04c80 100644 --- a/monte-cover/src/montecover/ssm/ssm_nonig_ate.py +++ b/monte-cover/src/montecover/ssm/ssm_nonig_ate.py @@ -32,7 +32,9 @@ def __init__( def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - assert "learners" in self.dml_parameters, "No learners specified in the config file" + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" required_learners = ["ml_g", "ml_m", "ml_pi"] for learner in self.dml_parameters["learners"]: @@ -46,7 +48,9 @@ def _calculate_oracle_values(self): self.oracle_values = dict() self.oracle_values["theta"] = self.dgp_parameters["theta"] - def run_single_rep(self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any]) -> Dict[str, Any]: + def run_single_rep( + self, dml_data: dml.DoubleMLData, dml_params: Dict[str, Any] + ) -> Dict[str, Any]: """Run a single repetition with the given parameters.""" # Extract parameters learner_config = dml_params["learners"] @@ -107,7 +111,9 @@ def summarize_results(self): # Aggregate results (possibly multiple result dfs) result_summary = dict() for result_name, result_df in self.results.items(): - result_summary[result_name] = result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) self.logger.debug(f"Summarized {result_name} results") return result_summary diff --git a/monte-cover/src/montecover/utils.py b/monte-cover/src/montecover/utils.py index 838cb431..de27dd4f 100644 --- a/monte-cover/src/montecover/utils.py +++ b/monte-cover/src/montecover/utils.py @@ -2,7 +2,12 @@ from doubleml.utils import GlobalClassifier, GlobalRegressor from lightgbm import LGBMClassifier, LGBMRegressor -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, StackingClassifier, StackingRegressor +from sklearn.ensemble import ( + RandomForestClassifier, + RandomForestRegressor, + StackingClassifier, + StackingRegressor, +) from sklearn.linear_model import LassoCV, LinearRegression, LogisticRegression, Ridge LearnerInstantiator = Callable[[Dict[str, Any]], Any] @@ -11,8 +16,12 @@ "LassoCV": lambda params: LassoCV(**params), "RF Regr.": lambda params: RandomForestRegressor(**params), "RF Clas.": lambda params: RandomForestClassifier(**params), - "LGBM Regr.": lambda params: LGBMRegressor(**{**{"verbose": -1, "n_jobs": 1}, **params}), - "LGBM Clas.": lambda params: LGBMClassifier(**{**{"verbose": -1, "n_jobs": 1}, **params}), + "LGBM Regr.": lambda params: LGBMRegressor( + **{**{"verbose": -1, "n_jobs": 1}, **params} + ), + "LGBM Clas.": lambda params: LGBMClassifier( + **{**{"verbose": -1, "n_jobs": 1}, **params} + ), "Linear": lambda params: LinearRegression(**params), "Logistic": lambda params: LogisticRegression(**params), "Global Linear": lambda params: GlobalRegressor(LinearRegression(**params)), From 375333cf5168066321a0424ca2302138bba244ba Mon Sep 17 00:00:00 2001 From: Sven Klaassen <47529404+SvenKlaassen@users.noreply.github.com> Date: Thu, 27 Nov 2025 16:17:12 +0100 Subject: [PATCH 11/51] add tuning results to docs --- doc/irm/apo.qmd | 123 +++++++++++++++++++++++++++++++++++++++++++++-- doc/plm/lplr.qmd | 91 +++++++++++++++++++++++++++++++++++ doc/plm/plr.qmd | 11 +++++ 3 files changed, 222 insertions(+), 3 deletions(-) diff --git a/doc/irm/apo.qmd b/doc/irm/apo.qmd index de63d456..9b3333e8 100644 --- a/doc/irm/apo.qmd +++ b/doc/irm/apo.qmd @@ -22,7 +22,9 @@ from utils.style_tables import generate_and_show_styled_table init_notebook_mode(all_interactive=True) ``` -## APO Pointwise Coverage +## Coverage + +### APO Pointwise Coverage The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. @@ -78,7 +80,7 @@ generate_and_show_styled_table( ``` -## APOS Coverage +### APOS Coverage The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. @@ -134,7 +136,7 @@ generate_and_show_styled_table( ) ``` -## Causal Contrast Coverage +### Causal Contrast Coverage The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. @@ -189,3 +191,118 @@ generate_and_show_styled_table( coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) ``` + + +## Tuning + +The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration. + +### APOS Coverage + +The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/irm/apos_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data +df_apos = pd.read_csv("../../results/irm/apos_tune_coverage.csv", index_col=None) + +assert df_apos["repetition"].nunique() == 1 +n_rep_apos = df_apos["repetition"].unique()[0] + +display_columns_apos = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_apos, + filters={"level": 0.95}, + display_cols=display_columns_apos, + n_rep=n_rep_apos, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_apos, + filters={"level": 0.9}, + display_cols=display_columns_apos, + n_rep=n_rep_apos, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + + +### Causal Contrast Coverage + +The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). + + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/irm/apos_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data +df_contrast = pd.read_csv("../../results/irm/apos_tune_causal_contrast.csv", index_col=None) + +assert df_contrast["repetition"].nunique() == 1 +n_rep_contrast = df_contrast["repetition"].unique()[0] + +display_columns_contrast = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_contrast, + filters={"level": 0.95}, + display_cols=display_columns_contrast, + n_rep=n_rep_contrast, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_contrast, + filters={"level": 0.9}, + display_cols=display_columns_contrast, + n_rep=n_rep_contrast, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` \ No newline at end of file diff --git a/doc/plm/lplr.qmd b/doc/plm/lplr.qmd index 9a0eece9..691a6c81 100644 --- a/doc/plm/lplr.qmd +++ b/doc/plm/lplr.qmd @@ -111,3 +111,94 @@ generate_and_show_styled_table( coverage_highlight_cols=["Coverage"] ) ``` + + +## Tuning + +The simulations are based on the the [make_lplr_LZZ2020](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_lplr_LZZ2020.html)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration. + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/plm/lplr_ate_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data and rename columns +df_coverage = pd.read_csv("../../results/plm/lplr_ate_tune_coverage.csv", index_col=None) + +if "repetition" in df_coverage.columns and df_coverage["repetition"].nunique() == 1: + n_rep_coverage = df_coverage["repetition"].unique()[0] +elif "n_rep" in df_coverage.columns and df_coverage["n_rep"].nunique() == 1: + n_rep_coverage = df_coverage["n_rep"].unique()[0] +else: + n_rep_coverage = "N/A" # Fallback if n_rep cannot be determined + +display_columns_coverage = ["Learner m", "Learner M", "Learner t", "Tuned", "Bias", "CI Length", "Coverage"] +``` + +### Nuisance space + +```{python} +# | echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.95, "Score": "nuisance_space"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", +# rename_map={"Learner g": "Learner l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.9, "Score": "nuisance_space"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", +# rename_map={"Learner g": "Learner l"}, + coverage_highlight_cols=["Coverage"] +) +``` + +### Instrument + + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.95, "Score": "instrument"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` + +```{python} +#| echo: false + +generate_and_show_styled_table( + main_df=df_coverage, + filters={"level": 0.9, "Score": "instrument"}, + display_cols=display_columns_coverage, + n_rep=n_rep_coverage, + level_col="level", + coverage_highlight_cols=["Coverage"] +) +``` \ No newline at end of file diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd index 6c1c366f..a64e626f 100644 --- a/doc/plm/plr.qmd +++ b/doc/plm/plr.qmd @@ -213,6 +213,17 @@ generate_and_show_styled_table( The simulations are based on the the [make_plr_CCDDHNR2018](https://docs.doubleml.org/stable/api/generated/doubleml.plm.datasets.make_plr_CCDDHNR2018.html)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration. +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/plm/plr_ate_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + ```{python} #| echo: false From c31b082d81f060da41bdfd7627f02365ab0109b2 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Thu, 27 Nov 2025 21:15:21 +0100 Subject: [PATCH 12/51] add sim for did_pa_multi_tune --- monte-cover/src/montecover/did/__init__.py | 7 +- .../src/montecover/did/did_pa_multi_tune.py | 248 ++++++++++++++++++ results/did/did_pa_multi_tune_config.yml | 27 ++ results/did/did_pa_multi_tune_detailed.csv | 5 + results/did/did_pa_multi_tune_eventstudy.csv | 5 + results/did/did_pa_multi_tune_group.csv | 5 + results/did/did_pa_multi_tune_metadata.csv | 2 + results/did/did_pa_multi_tune_time.csv | 5 + scripts/did/did_pa_multi_tune.py | 13 + scripts/did/did_pa_multi_tune_config.yml | 34 +++ 10 files changed, 350 insertions(+), 1 deletion(-) create mode 100644 monte-cover/src/montecover/did/did_pa_multi_tune.py create mode 100644 results/did/did_pa_multi_tune_config.yml create mode 100644 results/did/did_pa_multi_tune_detailed.csv create mode 100644 results/did/did_pa_multi_tune_eventstudy.csv create mode 100644 results/did/did_pa_multi_tune_group.csv create mode 100644 results/did/did_pa_multi_tune_metadata.csv create mode 100644 results/did/did_pa_multi_tune_time.csv create mode 100644 scripts/did/did_pa_multi_tune.py create mode 100644 scripts/did/did_pa_multi_tune_config.yml diff --git a/monte-cover/src/montecover/did/__init__.py b/monte-cover/src/montecover/did/__init__.py index e14a6ddb..6d0f2da5 100644 --- a/monte-cover/src/montecover/did/__init__.py +++ b/monte-cover/src/montecover/did/__init__.py @@ -2,5 +2,10 @@ from montecover.did.did_cs_multi import DIDCSMultiCoverageSimulation from montecover.did.did_pa_multi import DIDMultiCoverageSimulation +from montecover.did.did_pa_multi_tune import DIDMultiTuningCoverageSimulation -__all__ = ["DIDMultiCoverageSimulation", "DIDCSMultiCoverageSimulation"] +__all__ = [ + "DIDMultiCoverageSimulation", + "DIDCSMultiCoverageSimulation", + "DIDMultiTuningCoverageSimulation" +] diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py new file mode 100644 index 00000000..3e4112c0 --- /dev/null +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -0,0 +1,248 @@ +from typing import Any, Dict, Optional + +import doubleml as dml +import numpy as np +import optuna +import pandas as pd +from doubleml.did.datasets import make_did_CS2021 + +from montecover.base import BaseSimulation +from montecover.utils import create_learner_from_config + + +class DIDMultiTuningCoverageSimulation(BaseSimulation): + """Simulation study for coverage properties of DoubleMLDIDMulti with hyperparameter tuning.""" + + def __init__( + self, + config_file: str, + suppress_warnings: bool = True, + log_level: str = "INFO", + log_file: Optional[str] = None, + ): + super().__init__( + config_file=config_file, + suppress_warnings=suppress_warnings, + log_level=log_level, + log_file=log_file, + ) + + # Additional results storage for aggregated results + self.results_aggregated = [] + + # Calculate oracle values + self._calculate_oracle_values() + + # tuning specific settings + # parameter space for the outcome regression tuning + def ml_g_params(trial): + return { + "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 50, step=5 + ), + "max_depth": 5, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), + } + + # parameter space for the propensity score tuning + def ml_m_params(trial): + return { + "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), + "learning_rate": trial.suggest_float( + "learning_rate", 1e-3, 0.1, log=True + ), + "min_child_samples": trial.suggest_int( + "min_child_samples", 20, 50, step=5 + ), + "max_depth": 5, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), + } + + self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} + + self._optuna_settings = { + "n_trials": 200, + "show_progress_bar": False, + "verbosity": optuna.logging.WARNING, # Suppress Optuna logs + } + + def _process_config_parameters(self): + """Process simulation-specific parameters from config""" + # Process ML models in parameter grid + # Process ML models in parameter grid + assert ( + "learners" in self.dml_parameters + ), "No learners specified in the config file" + + required_learners = ["ml_g", "ml_m"] + for learner in self.dml_parameters["learners"]: + for ml in required_learners: + assert ml in learner, f"No {ml} specified in the config file" + + def _calculate_oracle_values(self): + """Calculate oracle values for the simulation.""" + self.logger.info("Calculating oracle values") + + self.oracle_values = dict() + # Oracle values + df_oracle = make_did_CS2021( + n_obs=int(1e6), dgp_type=1 + ) # does not depend on the DGP type + df_oracle["ite"] = df_oracle["y1"] - df_oracle["y0"] + self.oracle_values["detailed"] = ( + df_oracle.groupby(["d", "t"])["ite"].mean().reset_index() + ) + + # Oracle group aggregation + df_oracle_post_treatment = df_oracle[df_oracle["t"] >= df_oracle["d"]] + self.oracle_values["group"] = df_oracle_post_treatment.groupby("d")[ + "ite" + ].mean() + + # Oracle time aggregation + self.oracle_values["time"] = df_oracle_post_treatment.groupby("t")["ite"].mean() + + # Oracle eventstudy aggregation + df_oracle["e"] = pd.to_datetime(df_oracle["t"]).values.astype( + "datetime64[M]" + ) - pd.to_datetime(df_oracle["d"]).values.astype("datetime64[M]") + self.oracle_values["eventstudy"] = df_oracle.groupby("e")["ite"].mean()[1:] + + def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: + """Run a single repetition with the given parameters.""" + # Extract parameters + learner_config = dml_params["learners"] + learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) + learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) + score = dml_params["score"] + in_sample_normalization = dml_params["in_sample_normalization"] + + # Model + dml_model = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=None if score == "experimental" else ml_m, + gt_combinations="standard", + score=score, + in_sample_normalization=in_sample_normalization, + ) + # Tuning + dml_model_tuned = dml.did.DoubleMLDIDMulti( + obj_dml_data=dml_data, + ml_g=ml_g, + ml_m=None if score == "experimental" else ml_m, + gt_combinations="standard", + score=score, + in_sample_normalization=in_sample_normalization, + ) + dml_model_tuned.tune_ml_models( + ml_param_space=self._param_space, + optuna_settings=self._optuna_settings, + ) + + # sort out oracle thetas + oracle_thetas = np.full(len(dml_model.gt_combinations), np.nan) + for i, (g, _, t) in enumerate(dml_model.gt_combinations): + group_index = self.oracle_values["detailed"]["d"] == g + time_index = self.oracle_values["detailed"]["t"] == t + oracle_thetas[i] = self.oracle_values["detailed"][group_index & time_index][ + "ite" + ].iloc[0] + + result = { + "detailed": [], + "group": [], + "time": [], + "eventstudy": [], + } + for model in [dml_model, dml_model_tuned]: + model.fit() + model.bootstrap(n_rep_boot=2000) + for level in self.confidence_parameters["level"]: + level_result = dict() + level_result["detailed"] = self._compute_coverage( + thetas=model.coef, + oracle_thetas=oracle_thetas, + confint=model.confint(level=level), + joint_confint=model.confint(level=level, joint=True), + ) + + for aggregation_method in ["group", "time", "eventstudy"]: + agg_obj = model.aggregate(aggregation=aggregation_method) + agg_obj.aggregated_frameworks.bootstrap(n_rep_boot=2000) + + level_result[aggregation_method] = self._compute_coverage( + thetas=agg_obj.aggregated_frameworks.thetas, + oracle_thetas=self.oracle_values[aggregation_method].values, + confint=agg_obj.aggregated_frameworks.confint(level=level), + joint_confint=agg_obj.aggregated_frameworks.confint( + level=level, joint=True + ), + ) + + # add parameters to the result + for res in level_result.values(): + res.update( + { + "Learner g": learner_g_name, + "Learner m": learner_m_name, + "Score": score, + "In-sample-norm.": in_sample_normalization, + "level": level, + "Tuned": model is dml_model_tuned, + } + ) + for key, res in level_result.items(): + result[key].append(res) + + return result + + def summarize_results(self): + """Summarize the simulation results.""" + self.logger.info("Summarizing simulation results") + + groupby_cols = [ + "Learner g", + "Learner m", + "Score", + "In-sample-norm.", + "DGP", + "level", + "Tuned", + ] + aggregation_dict = { + "Coverage": "mean", + "CI Length": "mean", + "Bias": "mean", + "Uniform Coverage": "mean", + "Uniform CI Length": "mean", + "repetition": "count", + } + + result_summary = dict() + for result_name, result_df in self.results.items(): + result_summary[result_name] = ( + result_df.groupby(groupby_cols).agg(aggregation_dict).reset_index() + ) + self.logger.debug(f"Summarized {result_name} results") + + return result_summary + + def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData: + """Generate data for the simulation.""" + data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"]) + dml_data = dml.data.DoubleMLPanelData( + data, + y_col="y", + d_cols="d", + id_col="id", + t_col="t", + x_cols=["Z1", "Z2", "Z3", "Z4"], + ) + return dml_data diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml new file mode 100644 index 00000000..549aef92 --- /dev/null +++ b/results/did/did_pa_multi_tune_config.yml @@ -0,0 +1,27 @@ +simulation_parameters: + repetitions: 2 + max_runtime: 19800 + random_seed: 42 + n_jobs: -2 +dgp_parameters: + DGP: + - 1 + n_obs: + - 2000 +learner_definitions: + lgbmr: &id001 + name: LGBM Regr. + lgbmc: &id002 + name: LGBM Clas. +dml_parameters: + learners: + - ml_g: *id001 + ml_m: *id002 + score: + - observational + in_sample_normalization: + - true +confidence_parameters: + level: + - 0.95 + - 0.9 diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv new file mode 100644 index 00000000..7def1ef2 --- /dev/null +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9166666666666667,0.9768034697095042,0.23400349839993873,1.0,1.5475751781406841,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.875,0.606470751441224,0.1614408775625408,0.5,0.9452913645347121,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.163933124038929,0.23400349839993873,1.0,1.6901781219508825,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.875,0.7226544727294515,0.1614408775625408,0.5,1.033662115539573,2 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv new file mode 100644 index 00000000..3f2d3ae2 --- /dev/null +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,1.0,0.8932471261067743,0.22134836624169563,1.0,1.2842220318304163,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.5517000133722438,0.171384287662864,0.5,0.7722944107543078,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.0643695996876914,0.22134836624169563,1.0,1.4044272167279779,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9166666666666667,0.6573911129611814,0.171384287662864,0.5,0.8460573095770816,2 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv new file mode 100644 index 00000000..afba1350 --- /dev/null +++ b/results/did/did_pa_multi_tune_group.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8333333333333333,0.9252764729189984,0.27505916642335154,1.0,1.1910838199026417,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.6185581636672488,0.18895139098415115,0.5,0.7900541517154416,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.10253492040176,0.27505916642335154,1.0,1.328849771012091,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.8333333333333333,0.7370575127575214,0.18895139098415115,0.5,0.8906147435536307,2 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv new file mode 100644 index 00000000..a3a12faa --- /dev/null +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -0,0 +1,2 @@ +DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-27 21:14,8.742515516281127,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv new file mode 100644 index 00000000..963e5b83 --- /dev/null +++ b/results/did/did_pa_multi_tune_time.csv @@ -0,0 +1,5 @@ +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8333333333333333,0.9347149637122762,0.21239079355381435,1.0,1.1850129288693911,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.5539493870014797,0.16522070900786323,1.0,0.6750979119813514,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.1137815758610201,0.21239079355381435,1.0,1.299771642249359,2 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,1.0,0.6600714069574607,0.16522070900786323,1.0,0.7914255322117764,2 diff --git a/scripts/did/did_pa_multi_tune.py b/scripts/did/did_pa_multi_tune.py new file mode 100644 index 00000000..609b40da --- /dev/null +++ b/scripts/did/did_pa_multi_tune.py @@ -0,0 +1,13 @@ +from montecover.did import DIDMultiTuningCoverageSimulation + +# Create and run simulation with config file +sim = DIDMultiTuningCoverageSimulation( + config_file="scripts/did/did_pa_multi_tune_config.yml", + log_level="DEBUG", + log_file="logs/did/did_pa_multi_tune_sim.log", +) +sim.run_simulation() +sim.save_results(output_path="results/did/", file_prefix="did_pa_multi_tune") + +# Save config file for reproducibility +sim.save_config("results/did/did_pa_multi_tune_config.yml") diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml new file mode 100644 index 00000000..df71d1ef --- /dev/null +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -0,0 +1,34 @@ +# Simulation parameters for DID Multi Coverage + +simulation_parameters: + repetitions: 2 + max_runtime: 19800 # 5.5 hours in seconds + random_seed: 42 + n_jobs: -2 + +dgp_parameters: + DGP: [1] # Different DGP specifications + n_obs: [2000] # Sample size for each simulation (has to be a list) + +# Define reusable learner configurations +learner_definitions: + lgbmr: &lgbmr + name: "LGBM Regr." + + lgbmc: &lgbmc + name: "LGBM Clas." + + +dml_parameters: + # ML methods for ml_g and ml_m + learners: + - ml_g: *lgbmr + ml_m: *lgbmc + + score: + - observational # Standard DML score + + in_sample_normalization: [true] + +confidence_parameters: + level: [0.95, 0.90] # Confidence levels From 1c734f3352631593909ac8041b83e8b77ed5a1fd Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 28 Nov 2025 08:07:59 +0100 Subject: [PATCH 13/51] add did pa multi tune --- doc/did/did_pa_multi.qmd | 189 ++++++++++++++++++ .../src/montecover/did/did_pa_multi_tune.py | 30 ++- results/did/did_pa_multi_tune_config.yml | 4 +- results/did/did_pa_multi_tune_detailed.csv | 8 +- results/did/did_pa_multi_tune_eventstudy.csv | 8 +- results/did/did_pa_multi_tune_group.csv | 8 +- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 8 +- scripts/did/did_pa_multi_tune_config.yml | 4 +- 9 files changed, 223 insertions(+), 38 deletions(-) diff --git a/doc/did/did_pa_multi.qmd b/doc/did/did_pa_multi.qmd index 22cb5de7..4270ac8a 100644 --- a/doc/did/did_pa_multi.qmd +++ b/doc/did/did_pa_multi.qmd @@ -320,3 +320,192 @@ generate_and_show_styled_table( coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) ``` + + +## Tuning + +The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Due to time constraints we only consider one learner, use in-sample normalization and the following DGPs: + + - Type 4: Nonlinear outcome model and treatment assignment + +The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. + +::: {.callout-note title="Metadata" collapse="true"} + +```{python} +#| echo: false +metadata_file = '../../results/did/did_pa_multi_tune_metadata.csv' +metadata_df = pd.read_csv(metadata_file) +print(metadata_df.T.to_string(header=False)) +``` + +::: + +```{python} +#| echo: false + +# set up data +df = pd.read_csv("../../results/did/did_pa_multi_tune_detailed.csv", index_col=None) + +assert df["repetition"].nunique() == 1 +n_rep = df["repetition"].unique()[0] + +display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +### Observational Score + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df, + filters={"level": 0.95, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df, + filters={"level": 0.9, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +## Tuning Aggregated Effects + +These simulations test different types of aggregation, as described in [DiD User Guide](https://docs.doubleml.org/stable/guide/models.html#difference-in-differences-models-did). + +As before, we only consider one learner, use in-sample normalization and the following DGPs: + + - Type 4: Nonlinear outcome model and treatment assignment + +The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. + +### Group Effects + +```{python} +#| echo: false + +# set up data +df_group = pd.read_csv("../../results/did/did_pa_multi_tune_group.csv", index_col=None) + +assert df_group["repetition"].nunique() == 1 +n_rep_group = df_group["repetition"].unique()[0] + +display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +#### Observational Score + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_group, + filters={"level": 0.95, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_group, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_group, + filters={"level": 0.9, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_group, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + + +### Time Effects + +```{python} +#| echo: false + +# set up data +df_time = pd.read_csv("../../results/did/did_pa_multi_tune_time.csv", index_col=None) + +assert df_time["repetition"].nunique() == 1 +n_rep_time = df_time["repetition"].unique()[0] + +display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +#### Observational Score + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_time, + filters={"level": 0.95, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_time, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_time, + filters={"level": 0.9, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_time, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +### Event Study Aggregation + +```{python} +#| echo: false + +# set up data +df_es = pd.read_csv("../../results/did/did_pa_multi_tune_eventstudy.csv", index_col=None) + +assert df_es["repetition"].nunique() == 1 +n_rep_es = df_es["repetition"].unique()[0] + +display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +``` + +#### Observational Score + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_es, + filters={"level": 0.95, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_es, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` + +```{python} +#| echo: false +generate_and_show_styled_table( + main_df=df_es, + filters={"level": 0.9, "Score": "observational"}, + display_cols=display_columns, + n_rep=n_rep_es, + level_col="level", + coverage_highlight_cols=["Coverage", "Uniform Coverage"] +) +``` diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py index 3e4112c0..c063fe1b 100644 --- a/monte-cover/src/montecover/did/did_pa_multi_tune.py +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -37,37 +37,33 @@ def __init__( # parameter space for the outcome regression tuning def ml_g_params(trial): return { - "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), + "n_estimators": trial.suggest_int("n_estimators", 100, 300, step=25), + "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1), "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 50, step=5 + "min_child_samples", 5, 50, step=5 ), - "max_depth": 5, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), + "max_depth": 3, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-1, 10.0), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-1, 10.0), } # parameter space for the propensity score tuning def ml_m_params(trial): return { - "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), + "n_estimators": trial.suggest_int("n_estimators", 100, 300, step=25), + "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1), "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 50, step=5 + "min_child_samples", 5, 50, step=5 ), - "max_depth": 5, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), + "max_depth": 3, + "lambda_l1": trial.suggest_float("lambda_l1", 1e-1, 10.0), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-1, 10.0), } self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} self._optuna_settings = { - "n_trials": 200, + "n_trials": 50, "show_progress_bar": False, "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml index 549aef92..80ca70f4 100644 --- a/results/did/did_pa_multi_tune_config.yml +++ b/results/did/did_pa_multi_tune_config.yml @@ -1,11 +1,11 @@ simulation_parameters: - repetitions: 2 + repetitions: 50 max_runtime: 19800 random_seed: 42 n_jobs: -2 dgp_parameters: DGP: - - 1 + - 4 n_obs: - 2000 learner_definitions: diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index 7def1ef2..71a2f3fe 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9166666666666667,0.9768034697095042,0.23400349839993873,1.0,1.5475751781406841,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.875,0.606470751441224,0.1614408775625408,0.5,0.9452913645347121,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.163933124038929,0.23400349839993873,1.0,1.6901781219508825,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.875,0.7226544727294515,0.1614408775625408,0.5,1.033662115539573,2 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9316666666666668,1.4966891600161318,0.32660664033390235,0.92,2.3216053508736927,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7116666666666667,0.6931755373364055,0.26877949164802883,0.52,1.075386978472972,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9666666666666667,1.783415132883231,0.32660664033390235,0.98,2.5480137105876035,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.805,0.8259695974659736,0.26877949164802883,0.8,1.1805791952374205,50 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index 3f2d3ae2..cddd66df 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,1.0,0.8932471261067743,0.22134836624169563,1.0,1.2842220318304163,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.5517000133722438,0.171384287662864,0.5,0.7722944107543078,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.0643695996876914,0.22134836624169563,1.0,1.4044272167279779,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9166666666666667,0.6573911129611814,0.171384287662864,0.5,0.8460573095770816,2 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.93,1.4917723511590009,0.31094318906305424,1.0,2.046812212784216,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6133333333333334,0.6538130173979115,0.2872303658356806,0.56,0.9020402355007543,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9866666666666667,1.777556393770557,0.31094318906305424,1.0,2.302979981417753,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7533333333333333,0.7790662620225798,0.2872303658356806,0.7,1.0094757455236818,50 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index afba1350..f4915f47 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8333333333333333,0.9252764729189984,0.27505916642335154,1.0,1.1910838199026417,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.6185581636672488,0.18895139098415115,0.5,0.7900541517154416,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.10253492040176,0.27505916642335154,1.0,1.328849771012091,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.8333333333333333,0.7370575127575214,0.18895139098415115,0.5,0.8906147435536307,2 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9533333333333333,1.523584682406954,0.2957296523184597,0.98,1.9163577588137022,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.68,0.7074931257281757,0.29440880930589985,0.6,0.9006828631295234,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9933333333333333,1.8154631244903028,0.2957296523184597,0.98,2.1923219567713677,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7666666666666666,0.8430300563016622,0.29440880930589985,0.74,1.0223420230094116,50 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index a3a12faa..4a17782e 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-27 21:14,8.742515516281127,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-28 08:04,10.720669198036195,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index 963e5b83..3f622825 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8333333333333333,0.9347149637122762,0.21239079355381435,1.0,1.1850129288693911,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8333333333333333,0.5539493870014797,0.16522070900786323,1.0,0.6750979119813514,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,1.0,1.1137815758610201,0.21239079355381435,1.0,1.299771642249359,2 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,1.0,0.6600714069574607,0.16522070900786323,1.0,0.7914255322117764,2 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9533333333333333,1.5850230603285973,0.31390770251930433,0.96,1.9287985346498584,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.5333333333333333,0.6805542616676624,0.31796626809821743,0.46,0.8338119946003776,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9733333333333333,1.888671467179226,0.31390770251930433,0.98,2.212665951882592,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.68,0.8109304199097704,0.31796626809821743,0.62,0.9546449744673592,50 diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml index df71d1ef..1c13a58a 100644 --- a/scripts/did/did_pa_multi_tune_config.yml +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -1,13 +1,13 @@ # Simulation parameters for DID Multi Coverage simulation_parameters: - repetitions: 2 + repetitions: 50 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: - DGP: [1] # Different DGP specifications + DGP: [4] # Different DGP specifications n_obs: [2000] # Sample size for each simulation (has to be a list) # Define reusable learner configurations From 739350581c43d7e4187ff5cf6ea4263064dd099b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Fri, 28 Nov 2025 10:36:39 +0100 Subject: [PATCH 14/51] rerun did sim --- .../src/montecover/did/did_pa_multi_tune.py | 28 ++++++++----------- results/did/did_pa_multi_tune_detailed.csv | 8 +++--- results/did/did_pa_multi_tune_eventstudy.csv | 8 +++--- results/did/did_pa_multi_tune_group.csv | 8 +++--- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 8 +++--- 6 files changed, 29 insertions(+), 33 deletions(-) diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py index c063fe1b..63c4acef 100644 --- a/monte-cover/src/montecover/did/did_pa_multi_tune.py +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -37,27 +37,23 @@ def __init__( # parameter space for the outcome regression tuning def ml_g_params(trial): return { - "n_estimators": trial.suggest_int("n_estimators", 100, 300, step=25), - "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1), - "min_child_samples": trial.suggest_int( - "min_child_samples", 5, 50, step=5 - ), - "max_depth": 3, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-1, 10.0), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-1, 10.0), + 'n_estimators': 100, + 'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True), + 'max_depth': 3, + 'min_child_samples': trial.suggest_int('min_child_samples', 10, 20, step=5), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-2, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-2, 10.0, log=True), } # parameter space for the propensity score tuning def ml_m_params(trial): return { - "n_estimators": trial.suggest_int("n_estimators", 100, 300, step=25), - "learning_rate": trial.suggest_float("learning_rate", 0.005, 0.1), - "min_child_samples": trial.suggest_int( - "min_child_samples", 5, 50, step=5 - ), - "max_depth": 3, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-1, 10.0), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-1, 10.0), + 'n_estimators': 100, + 'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True), + 'max_depth': 3, + 'min_child_samples': trial.suggest_int('min_child_samples', 10, 20, step=5), + 'lambda_l1': trial.suggest_float('lambda_l1', 1e-2, 10.0, log=True), + 'lambda_l2': trial.suggest_float('lambda_l2', 1e-2, 10.0, log=True), } self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index 71a2f3fe..8d056afa 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9316666666666668,1.4966891600161318,0.32660664033390235,0.92,2.3216053508736927,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7116666666666667,0.6931755373364055,0.26877949164802883,0.52,1.075386978472972,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9666666666666667,1.783415132883231,0.32660664033390235,0.98,2.5480137105876035,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.805,0.8259695974659736,0.26877949164802883,0.8,1.1805791952374205,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.92,1.348642264453785,0.3052825096979891,0.92,2.098844496580318,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.7307170319438454,0.25209824311524026,0.58,1.1328574508344817,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.965,1.6070063761582027,0.3052825096979891,0.94,2.299873978116884,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8483333333333333,0.8707030473917022,0.25209824311524026,0.72,1.2446051133150078,50 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index cddd66df..625e0410 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.93,1.4917723511590009,0.31094318906305424,1.0,2.046812212784216,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6133333333333334,0.6538130173979115,0.2872303658356806,0.56,0.9020402355007543,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9866666666666667,1.777556393770557,0.31094318906305424,1.0,2.302979981417753,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7533333333333333,0.7790662620225798,0.2872303658356806,0.7,1.0094757455236818,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9133333333333333,1.3059006382995975,0.28281151638077767,0.92,1.8076721639449533,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6933333333333332,0.6935329291378785,0.27189850445260494,0.6,0.9561393211979365,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9633333333333333,1.5560765873124054,0.28281151638077767,0.96,2.0177850919183284,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7766666666666667,0.8263954560638327,0.27189850445260494,0.7,1.0722540700492953,50 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index f4915f47..3b9ed46e 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9533333333333333,1.523584682406954,0.2957296523184597,0.98,1.9163577588137022,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.68,0.7074931257281757,0.29440880930589985,0.6,0.9006828631295234,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9933333333333333,1.8154631244903028,0.2957296523184597,0.98,2.1923219567713677,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7666666666666666,0.8430300563016622,0.29440880930589985,0.74,1.0223420230094116,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.94,1.335317766310145,0.2896013491766717,0.96,1.698765271200942,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6666666666666667,0.7417801146292107,0.28018974004255187,0.64,0.9458331628750551,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.98,1.5911292573400337,0.2896013491766717,0.96,1.9275714352433053,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8066666666666668,0.8838855234892817,0.28018974004255187,0.74,1.0623714755405609,50 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index 4a17782e..8589b360 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-28 08:04,10.720669198036195,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-28 10:36,6.335572187105814,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index 3f622825..ecffe084 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9533333333333333,1.5850230603285973,0.31390770251930433,0.96,1.9287985346498584,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.5333333333333333,0.6805542616676624,0.31796626809821743,0.46,0.8338119946003776,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9733333333333333,1.888671467179226,0.31390770251930433,0.98,2.212665951882592,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.68,0.8109304199097704,0.31796626809821743,0.62,0.9546449744673592,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9266666666666667,1.3680691335956139,0.2862973732054817,0.9,1.6857878745010273,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6533333333333333,0.7330060851496288,0.29002355600549995,0.54,0.9000560955775124,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.96,1.630154918512653,0.2862973732054817,0.94,1.9313064378387346,50 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.74,0.8734306225196768,0.29002355600549995,0.62,1.0346551755913964,50 From 347e33dc275f151aecb8b2be951d0367b2e96601 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 10:29:32 +0100 Subject: [PATCH 15/51] rerun did sim --- results/did/did_pa_multi_tune_config.yml | 5 ++++- results/did/did_pa_multi_tune_detailed.csv | 20 ++++++++++++++++---- results/did/did_pa_multi_tune_eventstudy.csv | 20 ++++++++++++++++---- results/did/did_pa_multi_tune_group.csv | 20 ++++++++++++++++---- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 20 ++++++++++++++++---- scripts/did/did_pa_multi_tune_config.yml | 4 ++-- 7 files changed, 71 insertions(+), 20 deletions(-) diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml index 80ca70f4..0bcf1119 100644 --- a/results/did/did_pa_multi_tune_config.yml +++ b/results/did/did_pa_multi_tune_config.yml @@ -1,10 +1,13 @@ simulation_parameters: - repetitions: 50 + repetitions: 100 max_runtime: 19800 random_seed: 42 n_jobs: -2 dgp_parameters: DGP: + - 1 + - 2 + - 3 - 4 n_obs: - 2000 diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index 8d056afa..c0361bba 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,5 +1,17 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.92,1.348642264453785,0.3052825096979891,0.92,2.098844496580318,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.7307170319438454,0.25209824311524026,0.58,1.1328574508344817,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.965,1.6070063761582027,0.3052825096979891,0.94,2.299873978116884,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8483333333333333,0.8707030473917022,0.25209824311524026,0.72,1.2446051133150078,50 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9041666666666667,1.1400890857194432,0.28740542972213634,0.92,1.7825586546503152,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8966666666666667,0.6472337011214075,0.15944046942151935,0.89,1.0113932407840978,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9608333333333333,1.3584999361424834,0.28740542972213634,0.98,1.9530265671281697,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9516666666666667,0.7712265231342353,0.15944046942151935,0.94,1.108993599863019,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9091666666666667,1.228317976026286,0.29714569696579457,0.87,1.9154225471875832,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.9075,0.6999396898634899,0.16414917750405422,0.89,1.0923185146373655,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9625,1.4636311432990996,0.29714569696579457,0.96,2.1000879769164107,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.955,0.8340295823313698,0.16414917750405422,0.92,1.1991852306848672,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9116666666666667,1.3107305270692071,0.31985432471813957,0.93,2.043307297150825,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8908333333333333,0.7112998258902837,0.17517537461692242,0.84,1.1042718075016527,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9616666666666667,1.5618317547526317,0.31985432471813957,0.96,2.2409152941225887,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9391666666666667,0.8475660193171083,0.17517537461692242,0.9,1.2154039059944974,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9166666666666667,1.4374818316154423,0.33276716041579246,0.94,2.2272959804052928,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7691666666666667,0.7404367767653766,0.254447961458634,0.69,1.1489525496687707,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9675,1.712865249668844,0.33276716041579246,0.96,2.449349686911489,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8541666666666667,0.8822848376963073,0.254447961458634,0.79,1.2629079128831524,100 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index 625e0410..d6f6b58d 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,5 +1,17 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9133333333333333,1.3059006382995975,0.28281151638077767,0.92,1.8076721639449533,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6933333333333332,0.6935329291378785,0.27189850445260494,0.6,0.9561393211979365,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9633333333333333,1.5560765873124054,0.28281151638077767,0.96,2.0177850919183284,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7766666666666667,0.8263954560638327,0.27189850445260494,0.7,1.0722540700492953,50 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9283333333333332,1.073068168296971,0.2597502402941499,0.93,1.4984680319815546,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.905,0.5982631043246142,0.14657170491101748,0.88,0.8348004786911729,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9733333333333333,1.2786395873512446,0.2597502402941499,0.98,1.677549043525822,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9516666666666667,0.7128744579714308,0.14657170491101748,0.97,0.9341199957423918,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9183333333333333,1.184163621831536,0.28342610855647676,0.94,1.6513528430347295,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.895,0.6576942435782439,0.15332741525347127,0.9,0.9110830194425428,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9616666666666667,1.4110179851649454,0.28342610855647676,0.99,1.8462857744217072,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.95,0.7836910282660079,0.15332741525347127,0.95,1.0193279652523601,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9183333333333333,1.2647923551028497,0.2906734347752842,0.95,1.7525858381045432,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.885,0.6743634497620673,0.16589326251977687,0.88,0.9307320111152868,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9683333333333333,1.5070930466424515,0.2906734347752842,0.97,1.9677774556587133,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9383333333333332,0.8035536125323768,0.16589326251977687,0.93,1.0446188993963292,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9283333333333332,1.4064992969714387,0.314613746630164,0.91,1.9372254520475036,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7133333333333333,0.7088318207664741,0.26929824646603895,0.66,0.975482632053878,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9683333333333333,1.6759472825883586,0.314613746630164,0.98,2.1745712334757688,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8033333333333332,0.8446252098267877,0.26929824646603895,0.76,1.0954531498790554,100 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index 3b9ed46e..a36433f0 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,5 +1,17 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.94,1.335317766310145,0.2896013491766717,0.96,1.698765271200942,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6666666666666667,0.7417801146292107,0.28018974004255187,0.64,0.9458331628750551,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.98,1.5911292573400337,0.2896013491766717,0.96,1.9275714352433053,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8066666666666668,0.8838855234892817,0.28018974004255187,0.74,1.0623714755405609,50 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8933333333333333,1.104533138085938,0.2786392828405217,0.93,1.403347738672554,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.9166666666666667,0.6434665238707676,0.15552856896641956,0.91,0.8209959260997447,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9666666666666667,1.3161324113633812,0.2786392828405217,0.97,1.5959470400469875,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9533333333333333,0.7667376545725277,0.15552856896641956,0.96,0.9301867858119841,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.92,1.2001857652221994,0.2761379784298203,0.94,1.5248040252023016,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.91,0.705320543228418,0.15481408501446786,0.88,0.8960249286786839,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.96,1.430109546557577,0.2761379784298203,0.97,1.7238479263548212,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.95,0.8404412645798971,0.15481408501446786,0.93,1.0186697969238927,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.8933333333333333,1.3085128545125806,0.30695508841663705,0.92,1.6601995824415863,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.89,0.7237290131511886,0.16938601624368954,0.91,0.9191510186073212,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9533333333333333,1.5591892349142273,0.30695508841663705,0.96,1.8814562718978545,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9533333333333333,0.8623763094178922,0.16938601624368954,0.92,1.042629687574485,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.4575276036298743,0.3136429990461747,0.93,1.841601446334188,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.7563191729586094,0.28371438189486503,0.66,0.9629096996466265,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9733333333333333,1.7367512602820832,0.3136429990461747,0.97,2.0888217668673956,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8233333333333333,0.9012098800325217,0.28371438189486503,0.81,1.087106451467499,100 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index 8589b360..a62f187f 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-11-28 10:36,6.335572187105814,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 10:11,45.433327130476634,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index ecffe084..e9325fbb 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,5 +1,17 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9266666666666667,1.3680691335956139,0.2862973732054817,0.9,1.6857878745010273,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6533333333333333,0.7330060851496288,0.29002355600549995,0.54,0.9000560955775124,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.96,1.630154918512653,0.2862973732054817,0.94,1.9313064378387346,50 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.74,0.8734306225196768,0.29002355600549995,0.62,1.0346551755913964,50 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9,1.1113763663388696,0.2783388170117924,0.92,1.3788558172615877,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8833333333333333,0.6148427992241794,0.15024436835827917,0.91,0.7634587423773123,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.96,1.3242866207677713,0.2783388170117924,0.97,1.5683457246716077,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.95,0.732630382964001,0.15024436835827917,0.99,0.8733603074599771,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.93,1.2340740537756638,0.2827466455736652,0.94,1.5228025759168033,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.93,0.6909121668851053,0.14601582394245094,0.91,0.8519331823086561,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.97,1.470489932978703,0.2827466455736652,0.97,1.7440031028399017,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9533333333333333,0.8232726252275122,0.14601582394245094,0.97,0.9758565772068002,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9433333333333332,1.2959603630389032,0.2926620156379758,0.96,1.6022423636178649,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8933333333333333,0.7034275560361475,0.16697163725248976,0.88,0.8670910640659377,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.98,1.5442320187817185,0.2926620156379758,0.98,1.834795211566282,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9533333333333333,0.8381856312157769,0.16697163725248976,0.96,0.9914556000230386,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9366666666666668,1.4986674065988734,0.3275028156163638,0.95,1.8415020458286135,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.67,0.7464049203833408,0.300651425058323,0.58,0.9150101228510614,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9833333333333333,1.785772359077211,0.3275028156163638,0.96,2.1067443351244504,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7733333333333333,0.8893963194440494,0.300651425058323,0.67,1.046760454668414,100 diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml index 1c13a58a..b46d6475 100644 --- a/scripts/did/did_pa_multi_tune_config.yml +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -1,13 +1,13 @@ # Simulation parameters for DID Multi Coverage simulation_parameters: - repetitions: 50 + repetitions: 100 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: - DGP: [4] # Different DGP specifications + DGP: [1,2,3,4] # Different DGP specifications n_obs: [2000] # Sample size for each simulation (has to be a list) # Define reusable learner configurations From 3525aeb319434908cecb07693b93f982bdf0c43b Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 12:07:13 +0100 Subject: [PATCH 16/51] rerun irm sim with updated nuisance loss logging --- doc/irm/irm.qmd | 8 ++++---- .../src/montecover/irm/irm_ate_tune.py | 19 +++++++++++++++---- results/irm/irm_ate_tune_config.yml | 4 ++-- results/irm/irm_ate_tune_coverage.csv | 10 +++++----- results/irm/irm_ate_tune_metadata.csv | 2 +- scripts/irm/irm_ate_tune_config.yml | 4 ++-- 6 files changed, 29 insertions(+), 18 deletions(-) diff --git a/doc/irm/irm.qmd b/doc/irm/irm.qmd index 272ae190..f347ae8e 100644 --- a/doc/irm/irm.qmd +++ b/doc/irm/irm.qmd @@ -251,7 +251,7 @@ generate_and_show_styled_table( ## Tuning -The simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_irm_data.html)-DGP with $1000$ observations. This is only an example as the untuned version just relies on the default configuration. +The simulations are based on the the [make_irm_data](https://docs.doubleml.org/stable/api/generated/doubleml.irm.datasets.make_irm_data.html)-DGP with $500$ observations. This is only an example as the untuned version just relies on the default configuration. ::: {.callout-note title="Metadata" collapse="true"} @@ -275,7 +275,7 @@ df_ate_tune_cov = pd.read_csv("../../results/irm/irm_ate_tune_coverage.csv", ind assert df_ate_tune_cov["repetition"].nunique() == 1 n_rep_ate_tune_cov = df_ate_tune_cov["repetition"].unique()[0] -display_columns_ate_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage",] +display_columns_ate_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Loss g0", "Loss g1", "Loss m"] ``` @@ -286,7 +286,7 @@ generate_and_show_styled_table( main_df=df_ate_tune_cov, filters={"level": 0.95}, display_cols=display_columns_ate_tune_cov, - n_rep=n_rep_ate_cov, + n_rep=n_rep_ate_tune_cov, level_col="level", coverage_highlight_cols=["Coverage"] ) @@ -300,7 +300,7 @@ generate_and_show_styled_table( main_df=df_ate_tune_cov, filters={"level": 0.9}, display_cols=display_columns_ate_tune_cov, - n_rep=n_rep_ate_cov, + n_rep=n_rep_ate_tune_cov, level_col="level", coverage_highlight_cols=["Coverage"] ) diff --git a/monte-cover/src/montecover/irm/irm_ate_tune.py b/monte-cover/src/montecover/irm/irm_ate_tune.py index 4ebb947a..257adbf4 100644 --- a/monte-cover/src/montecover/irm/irm_ate_tune.py +++ b/monte-cover/src/montecover/irm/irm_ate_tune.py @@ -37,9 +37,11 @@ def ml_g_params(trial): "learning_rate", 1e-3, 0.1, log=True ), "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 100, step=5 + "min_child_samples", 10, 50, step=5 ), - "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "max_depth": 3, + "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), + "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } @@ -52,9 +54,11 @@ def ml_m_params(trial): "learning_rate", 1e-3, 0.1, log=True ), "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 100, step=5 + "min_child_samples", 10, 50, step=5 ), - "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), + "max_depth": 3, + "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), + "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), } @@ -118,6 +122,7 @@ def run_single_rep( "coverage": [], } for model in [dml_model, dml_model_tuned]: + nuisance_loss = model.nuisance_loss for level in self.confidence_parameters["level"]: level_result = dict() level_result["coverage"] = self._compute_coverage( @@ -135,6 +140,9 @@ def run_single_rep( "Learner m": learner_m_name, "level": level, "Tuned": model is dml_model_tuned, + "Loss g0": nuisance_loss["ml_g0"].mean(), + "Loss g1": nuisance_loss["ml_g1"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -152,6 +160,9 @@ def summarize_results(self): "Coverage": "mean", "CI Length": "mean", "Bias": "mean", + "Loss g0": "mean", + "Loss g1": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/results/irm/irm_ate_tune_config.yml b/results/irm/irm_ate_tune_config.yml index 285d88ba..36a6fd18 100644 --- a/results/irm/irm_ate_tune_config.yml +++ b/results/irm/irm_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 500 + repetitions: 200 max_runtime: 19800 random_seed: 42 n_jobs: -2 @@ -7,7 +7,7 @@ dgp_parameters: theta: - 0.5 n_obs: - - 1000 + - 500 dim_x: - 5 learner_definitions: diff --git a/results/irm/irm_ate_tune_coverage.csv b/results/irm/irm_ate_tune_coverage.csv index 5780228c..9f17e1f9 100644 --- a/results/irm/irm_ate_tune_coverage.csv +++ b/results/irm/irm_ate_tune_coverage.csv @@ -1,5 +1,5 @@ -Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,False,0.952,1.8938962143131024,0.38916017267237296,500 -LGBM Regr.,LGBM Clas.,0.9,True,0.82,0.31269268855505145,0.09112340834220442,500 -LGBM Regr.,LGBM Clas.,0.95,False,0.988,2.256716530692214,0.38916017267237296,500 -LGBM Regr.,LGBM Clas.,0.95,True,0.9,0.3725963196693502,0.09112340834220442,500 +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Loss g0,Loss g1,Loss m,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.92,2.643216056843617,0.6446011747627041,1.1132816524651437,1.1357364135690846,0.667476757878396,200 +LGBM Regr.,LGBM Clas.,0.9,True,0.89,0.5145312866755513,0.12860156717199023,0.9989179129452523,1.0613529528468826,0.5188432505665598,200 +LGBM Regr.,LGBM Clas.,0.95,False,0.985,3.1495861941059564,0.6446011747627041,1.1132816524651437,1.1357364135690846,0.667476757878396,200 +LGBM Regr.,LGBM Clas.,0.95,True,0.935,0.6131018433975747,0.12860156717199023,0.9989179129452523,1.0613529528468826,0.5188432505665598,200 diff --git a/results/irm/irm_ate_tune_metadata.csv b/results/irm/irm_ate_tune_metadata.csv index 1ba129ae..70a58b5d 100644 --- a/results/irm/irm_ate_tune_metadata.csv +++ b/results/irm/irm_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,IRMATETuningCoverageSimulation,2025-11-24 13:07,97.69014709790548,3.12.9,scripts/irm/irm_ate_tune_config.yml +0.12.dev0,IRMATETuningCoverageSimulation,2025-12-01 12:02,27.278138709068298,3.12.9,scripts/irm/irm_ate_tune_config.yml diff --git a/scripts/irm/irm_ate_tune_config.yml b/scripts/irm/irm_ate_tune_config.yml index ff0eb6fe..b8dcca1a 100644 --- a/scripts/irm/irm_ate_tune_config.yml +++ b/scripts/irm/irm_ate_tune_config.yml @@ -1,14 +1,14 @@ # Simulation parameters for IRM ATE Coverage with Tuning simulation_parameters: - repetitions: 500 + repetitions: 200 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: theta: [0.5] # Treatment effect - n_obs: [1000] # Sample size + n_obs: [500] # Sample size dim_x: [5] # Number of covariates # Define reusable learner configurations From 2334050bd78c380b56856da65e10a626f2da8e63 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 12:10:30 +0100 Subject: [PATCH 17/51] add LightGBM parameter tuning functions for regression and classification --- .../src/montecover/irm/irm_ate_tune.py | 37 +------------------ monte-cover/src/montecover/utils_tuning.py | 26 +++++++++++++ 2 files changed, 28 insertions(+), 35 deletions(-) create mode 100644 monte-cover/src/montecover/utils_tuning.py diff --git a/monte-cover/src/montecover/irm/irm_ate_tune.py b/monte-cover/src/montecover/irm/irm_ate_tune.py index 257adbf4..085767f0 100644 --- a/monte-cover/src/montecover/irm/irm_ate_tune.py +++ b/monte-cover/src/montecover/irm/irm_ate_tune.py @@ -6,6 +6,7 @@ from montecover.base import BaseSimulation from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params, lgbm_cls_params class IRMATETuningCoverageSimulation(BaseSimulation): @@ -29,41 +30,7 @@ def __init__( self._calculate_oracle_values() # tuning specific settings - # parameter space for the outcome regression tuning - def ml_g_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 10, 50, step=5 - ), - "max_depth": 3, - "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), - "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - } - - # parameter space for the propensity score tuning - def ml_m_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 10, 50, step=5 - ), - "max_depth": 3, - "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), - "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - } - - self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} + self._param_space = {"ml_g": lgbm_reg_params, "ml_m": lgbm_cls_params} self._optuna_settings = { "n_trials": 500, diff --git a/monte-cover/src/montecover/utils_tuning.py b/monte-cover/src/montecover/utils_tuning.py new file mode 100644 index 00000000..4503c921 --- /dev/null +++ b/monte-cover/src/montecover/utils_tuning.py @@ -0,0 +1,26 @@ +def lgbm_reg_params(trial): + """Parameter space for LightGBM regression tuning.""" + return { + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float("learning_rate", 1e-3, 0.1, log=True), + "min_child_samples": trial.suggest_int("min_child_samples", 10, 50, step=5), + "max_depth": 3, + "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), + "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), + } + + +def lgbm_cls_params(trial): + """Parameter space for LightGBM classification tuning.""" + return { + "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), + "learning_rate": trial.suggest_float("learning_rate", 1e-3, 0.1, log=True), + "min_child_samples": trial.suggest_int("min_child_samples", 10, 50, step=5), + "max_depth": 3, + "feature_fraction": trial.suggest_float("feature_fraction", 0.6, 1), + "bagging_fraction": trial.suggest_float("bagging_fraction", 0.6, 1), + "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), + "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), + } From 899ff88d1ea07fc2a70542e863133d49417fec5f Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 13:16:39 +0100 Subject: [PATCH 18/51] update APO models documentation and enhance APOSTuningCoverageSimulation with loss metrics and parameter tuning --- doc/irm/apo.qmd | 48 +++++++++---------- monte-cover/src/montecover/irm/apos_tune.py | 51 ++++++++------------- results/irm/apos_tune_causal_contrast.csv | 10 ++-- results/irm/apos_tune_coverage.csv | 10 ++-- results/irm/apos_tune_metadata.csv | 2 +- 5 files changed, 55 insertions(+), 66 deletions(-) diff --git a/doc/irm/apo.qmd b/doc/irm/apo.qmd index 9b3333e8..c9200fa8 100644 --- a/doc/irm/apo.qmd +++ b/doc/irm/apo.qmd @@ -84,7 +84,7 @@ generate_and_show_styled_table( The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. -The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). +The non-uniform results (coverage, ci length and bias) refer to averaged values over all levels (point-wise confidende intervals). ::: {.callout-note title="Metadata" collapse="true"} @@ -140,7 +140,7 @@ generate_and_show_styled_table( The simulations are based on the the [make_irm_data_discrete_treatments](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $500$ observations. Due to the linearity of the DGP, Lasso and Logit Regression are nearly optimal choices for the nuisance estimation. -The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). +The non-uniform results (coverage, ci length and bias) refer to averaged values over all levels (point-wise confidende intervals). ::: {.callout-note title="Metadata" collapse="true"} @@ -199,7 +199,7 @@ The simulations are based on the the [make_irm_data_discrete_treatments](https: ### APOS Coverage -The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). +The non-uniform results (coverage, ci length and bias) refer to averaged values over all levels (point-wise confidende intervals). The same holds for the loss values which are averaged over all treatment levels. ::: {.callout-note title="Metadata" collapse="true"} @@ -216,22 +216,22 @@ print(metadata_df.T.to_string(header=False)) #| echo: false # set up data -df_apos = pd.read_csv("../../results/irm/apos_tune_coverage.csv", index_col=None) +df_apos_tune = pd.read_csv("../../results/irm/apos_tune_coverage.csv", index_col=None) -assert df_apos["repetition"].nunique() == 1 -n_rep_apos = df_apos["repetition"].unique()[0] +assert df_apos_tune["repetition"].nunique() == 1 +n_rep_apos_tune = df_apos_tune["repetition"].unique()[0] -display_columns_apos = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns_apos_tune = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_apos, + main_df=df_apos_tune, filters={"level": 0.95}, - display_cols=display_columns_apos, - n_rep=n_rep_apos, + display_cols=display_columns_apos_tune, + n_rep=n_rep_apos_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -242,10 +242,10 @@ generate_and_show_styled_table( #| echo: false generate_and_show_styled_table( - main_df=df_apos, + main_df=df_apos_tune, filters={"level": 0.9}, - display_cols=display_columns_apos, - n_rep=n_rep_apos, + display_cols=display_columns_apos_tune, + n_rep=n_rep_apos_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -254,7 +254,7 @@ generate_and_show_styled_table( ### Causal Contrast Coverage -The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). +The non-uniform results (coverage, ci length and bias) refer to averaged values over all quantiles (point-wise confidende intervals). The same holds for the loss values which are averaged over all treatment levels. ::: {.callout-note title="Metadata" collapse="true"} @@ -272,22 +272,22 @@ print(metadata_df.T.to_string(header=False)) #| echo: false # set up data -df_contrast = pd.read_csv("../../results/irm/apos_tune_causal_contrast.csv", index_col=None) +df_contrast_tune = pd.read_csv("../../results/irm/apos_tune_causal_contrast.csv", index_col=None) -assert df_contrast["repetition"].nunique() == 1 -n_rep_contrast = df_contrast["repetition"].unique()[0] +assert df_contrast_tune["repetition"].nunique() == 1 +n_rep_contrast_tune = df_contrast_tune["repetition"].unique()[0] -display_columns_contrast = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns_contrast_tune = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_contrast, + main_df=df_contrast_tune, filters={"level": 0.95}, - display_cols=display_columns_contrast, - n_rep=n_rep_contrast, + display_cols=display_columns_contrast_tune, + n_rep=n_rep_contrast_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -298,10 +298,10 @@ generate_and_show_styled_table( #| echo: false generate_and_show_styled_table( - main_df=df_contrast, + main_df=df_contrast_tune, filters={"level": 0.9}, - display_cols=display_columns_contrast, - n_rep=n_rep_contrast, + display_cols=display_columns_contrast_tune, + n_rep=n_rep_contrast_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) diff --git a/monte-cover/src/montecover/irm/apos_tune.py b/monte-cover/src/montecover/irm/apos_tune.py index ece2ae38..d8abaf1d 100644 --- a/monte-cover/src/montecover/irm/apos_tune.py +++ b/monte-cover/src/montecover/irm/apos_tune.py @@ -8,6 +8,7 @@ from montecover.base import BaseSimulation from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params, lgbm_cls_params class APOSTuningCoverageSimulation(BaseSimulation): @@ -31,37 +32,7 @@ def __init__( self._calculate_oracle_values() # tuning specific settings - # parameter space for the outcome regression tuning - def ml_g_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 50, step=5 - ), - "max_depth": 5, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), - } - - # parameter space for the propensity score tuning - def ml_m_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 200, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 50, step=5 - ), - "max_depth": 5, - "lambda_l1": trial.suggest_float("lambda_l1", 1e-3, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-3, 10.0, log=True), - } - - self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} + self._param_space = {"ml_g": lgbm_reg_params, "ml_m": lgbm_cls_params} self._optuna_settings = { "n_trials": 200, @@ -155,6 +126,18 @@ def run_single_rep( model.bootstrap(n_rep_boot=2000) causal_contrast_model = model.causal_contrast(reference_levels=0) causal_contrast_model.bootstrap(n_rep_boot=2000) + + # average all nuisance losses over treatment levels + n_lvls = len(model.modellist) + loss_dict = { + "ml_g_d_lvl0": np.full(n_lvls, np.nan), + "ml_g_d_lvl1": np.full(n_lvls, np.nan), + "ml_m": np.full(n_lvls, np.nan) + } + for key in loss_dict.keys(): + for i_submodel, submodel in enumerate(model.modellist): + loss_dict[key][i_submodel] = submodel.nuisance_loss[key].mean() + for level in self.confidence_parameters["level"]: level_result = dict() level_result["coverage"] = self._compute_coverage( @@ -180,6 +163,9 @@ def run_single_rep( "Learner m": learner_m_name, "level": level, "Tuned": model is dml_model_tuned, + "Loss g_control": loss_dict["ml_g_d_lvl0"].mean(), + "Loss g_treated": loss_dict["ml_g_d_lvl1"].mean(), + "Loss m": loss_dict["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -199,6 +185,9 @@ def summarize_results(self): "Bias": "mean", "Uniform Coverage": "mean", "Uniform CI Length": "mean", + "Loss g_control": "mean", + "Loss g_treated": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/results/irm/apos_tune_causal_contrast.csv b/results/irm/apos_tune_causal_contrast.csv index 9c88e6bd..97cfd5f6 100644 --- a/results/irm/apos_tune_causal_contrast.csv +++ b/results/irm/apos_tune_causal_contrast.csv @@ -1,5 +1,5 @@ -Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,False,0.915,37.253979808129365,8.903634313073434,0.95,44.16923532073818,200 -LGBM Regr.,LGBM Clas.,0.9,True,0.835,4.834836017081165,1.3591722777708926,0.815,5.703081839207788,200 -LGBM Regr.,LGBM Clas.,0.95,False,0.98,44.39085491153563,8.903634313073434,0.985,50.67060073707776,200 -LGBM Regr.,LGBM Clas.,0.95,True,0.915,5.761062449185174,1.3591722777708926,0.895,6.546100271377481,200 +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.905,37.557614215103456,9.702752376788483,0.93,44.43580521211171,10.231838281825558,13.632270699354638,0.7977017509425842,200 +LGBM Regr.,LGBM Clas.,0.9,True,0.8625,4.281908161317747,1.1456270552515193,0.885,5.058837856166666,9.74905255354395,11.553230793169227,0.6041491925910187,200 +LGBM Regr.,LGBM Clas.,0.95,False,0.9625,44.75265762296555,9.702752376788483,0.975,51.03212790722511,10.231838281825558,13.632270699354638,0.7977017509425842,200 +LGBM Regr.,LGBM Clas.,0.95,True,0.945,5.102208271774997,1.1456270552515193,0.95,5.809070370902955,9.74905255354395,11.553230793169227,0.6041491925910187,200 diff --git a/results/irm/apos_tune_coverage.csv b/results/irm/apos_tune_coverage.csv index d01ff1f6..619f30c4 100644 --- a/results/irm/apos_tune_coverage.csv +++ b/results/irm/apos_tune_coverage.csv @@ -1,5 +1,5 @@ -Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,False,0.93,27.82005877126217,6.439524063849977,0.96,35.676118532942496,200 -LGBM Regr.,LGBM Clas.,0.9,True,0.885,6.300962875030208,1.5916287837149021,0.88,7.710244822536492,200 -LGBM Regr.,LGBM Clas.,0.95,False,0.9766666666666667,33.14964465289176,6.439524063849977,0.975,40.33245838311548,200 -LGBM Regr.,LGBM Clas.,0.95,True,0.9466666666666668,7.50806035298818,1.5916287837149021,0.94,8.829514768318946,200 +Learner g,Learner m,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,0.9,False,0.9133333333333333,28.052950188600192,7.055413388225961,0.945,35.926212976449165,10.231838281825558,13.632270699354638,0.7977017509425842,200 +LGBM Regr.,LGBM Clas.,0.9,True,0.8866666666666667,6.138357605002483,1.524949927232772,0.865,7.417779175659066,9.74905255354395,11.553230793169227,0.6041491925910187,200 +LGBM Regr.,LGBM Clas.,0.95,False,0.9766666666666667,33.42715189293536,7.055413388225961,0.98,40.556851028772705,10.231838281825558,13.632270699354638,0.7977017509425842,200 +LGBM Regr.,LGBM Clas.,0.95,True,0.945,7.31430422312426,1.524949927232772,0.94,8.537534779534262,9.74905255354395,11.553230793169227,0.6041491925910187,200 diff --git a/results/irm/apos_tune_metadata.csv b/results/irm/apos_tune_metadata.csv index 57cc25db..e36b86fb 100644 --- a/results/irm/apos_tune_metadata.csv +++ b/results/irm/apos_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,APOSTuningCoverageSimulation,2025-11-26 11:38,31.90539586544037,3.12.9,scripts/irm/apos_tune_config.yml +0.12.dev0,APOSTuningCoverageSimulation,2025-12-01 13:09,38.63118334611257,3.12.9,scripts/irm/apos_tune_config.yml From ef28d95d207c2b2ed7d561bbe101c3c2c06e26be Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 13:50:36 +0100 Subject: [PATCH 19/51] refactor: update PLR ATE tuning coverage simulation with loss metrics and reduce repetitions --- doc/plm/plr.qmd | 6 +-- .../src/montecover/plm/plr_ate_tune.py | 40 ++++--------------- results/plm/plr_ate_tune_config.yml | 2 +- results/plm/plr_ate_tune_coverage.csv | 10 ++--- results/plm/plr_ate_tune_metadata.csv | 2 +- scripts/plm/plr_ate_tune_config.yml | 2 +- 6 files changed, 19 insertions(+), 43 deletions(-) diff --git a/doc/plm/plr.qmd b/doc/plm/plr.qmd index a64e626f..2fe0aaf2 100644 --- a/doc/plm/plr.qmd +++ b/doc/plm/plr.qmd @@ -233,7 +233,7 @@ df_tune_cov = pd.read_csv("../../results/plm/plr_ate_tune_coverage.csv", index_c assert df_tune_cov["repetition"].nunique() == 1 n_rep_tune_cov = df_tune_cov["repetition"].unique()[0] -display_columns_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage",] +display_columns_tune_cov = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Loss g", "Loss m"] ``` @@ -248,7 +248,7 @@ generate_and_show_styled_table( display_cols=display_columns_tune_cov, n_rep=n_rep_tune_cov, level_col="level", - rename_map={"Learner g": "Learner l"}, + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -262,7 +262,7 @@ generate_and_show_styled_table( display_cols=display_columns_tune_cov, n_rep=n_rep_tune_cov, level_col="level", - rename_map={"Learner g": "Learner l"}, + rename_map={"Learner g": "Learner l", "Loss g": "Loss l"}, coverage_highlight_cols=["Coverage"] ) ``` diff --git a/monte-cover/src/montecover/plm/plr_ate_tune.py b/monte-cover/src/montecover/plm/plr_ate_tune.py index 1e8b268c..c50b5efd 100644 --- a/monte-cover/src/montecover/plm/plr_ate_tune.py +++ b/monte-cover/src/montecover/plm/plr_ate_tune.py @@ -6,6 +6,7 @@ from montecover.base import BaseSimulation from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params class PLRATETuningCoverageSimulation(BaseSimulation): @@ -29,40 +30,10 @@ def __init__( self._calculate_oracle_values() # tuning specific settings - # parameter space for the outcome regression tuning - def ml_l_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 100, step=5 - ), - "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - } - - # parameter space for the propensity score tuning - def ml_m_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 100, step=5 - ), - "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - } - - self._param_space = {"ml_l": ml_l_params, "ml_m": ml_m_params} + self._param_space = {"ml_l": lgbm_reg_params, "ml_m": lgbm_reg_params} self._optuna_settings = { - "n_trials": 500, + "n_trials": 200, "show_progress_bar": False, "verbosity": optuna.logging.WARNING, # Suppress Optuna logs } @@ -121,6 +92,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "coverage": [], } for model in [dml_model, dml_model_tuned]: + nuisance_loss = model.nuisance_loss for level in self.confidence_parameters["level"]: level_result = dict() level_result["coverage"] = self._compute_coverage( @@ -139,6 +111,8 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Score": score, "level": level, "Tuned": model is dml_model_tuned, + "Loss g": nuisance_loss["ml_l"].mean() if score == "partialling out" else nuisance_loss["ml_g"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -156,6 +130,8 @@ def summarize_results(self): "Coverage": "mean", "CI Length": "mean", "Bias": "mean", + "Loss g": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/results/plm/plr_ate_tune_config.yml b/results/plm/plr_ate_tune_config.yml index 9893ef17..17875c95 100644 --- a/results/plm/plr_ate_tune_config.yml +++ b/results/plm/plr_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 500 + repetitions: 200 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/plm/plr_ate_tune_coverage.csv b/results/plm/plr_ate_tune_coverage.csv index a6cb5fe8..fa1f6276 100644 --- a/results/plm/plr_ate_tune_coverage.csv +++ b/results/plm/plr_ate_tune_coverage.csv @@ -1,5 +1,5 @@ -Learner g,Learner m,Score,level,Tuned,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.81,0.1479047026981892,0.04545068026009308,500 -LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.894,0.1451364370890545,0.037424408565466506,500 -LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.898,0.17623932347696217,0.04545068026009308,500 -LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.942,0.1729407315508218,0.037424408565466506,500 +Learner g,Learner m,Score,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition +LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.805,0.14733663739723357,0.04709716445165978,1.2426113404810248,1.1115356809360686,200 +LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.845,0.14434980329449199,0.03915064046361367,1.1704317749756923,1.06174632188426,200 +LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.87,0.17556243192108348,0.04709716445165978,1.2426113404810248,1.1115356809360686,200 +LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.895,0.17200339957118416,0.03915064046361367,1.1704317749756923,1.06174632188426,200 diff --git a/results/plm/plr_ate_tune_metadata.csv b/results/plm/plr_ate_tune_metadata.csv index eedfd1ee..dafdeb83 100644 --- a/results/plm/plr_ate_tune_metadata.csv +++ b/results/plm/plr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLRATETuningCoverageSimulation,2025-11-24 15:37,119.77829658587774,3.12.9,scripts/plm/plr_ate_tune_config.yml +0.12.dev0,PLRATETuningCoverageSimulation,2025-12-01 13:43,18.97071567773819,3.12.9,scripts/plm/plr_ate_tune_config.yml diff --git a/scripts/plm/plr_ate_tune_config.yml b/scripts/plm/plr_ate_tune_config.yml index df0ecd25..25bba462 100644 --- a/scripts/plm/plr_ate_tune_config.yml +++ b/scripts/plm/plr_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 500 + repetitions: 200 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 From e3eb809966d23613224e30f97df2a79bb5730025 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 14:40:55 +0100 Subject: [PATCH 20/51] update lplr sim --- doc/plm/lplr.qmd | 42 +++++++++---------- .../src/montecover/did/did_pa_multi_tune.py | 33 ++++----------- .../src/montecover/plm/lplr_ate_tune.py | 33 ++++++--------- results/plm/lplr_ate_tune_coverage.csv | 18 ++++---- results/plm/lplr_ate_tune_metadata.csv | 2 +- 5 files changed, 51 insertions(+), 77 deletions(-) diff --git a/doc/plm/lplr.qmd b/doc/plm/lplr.qmd index 691a6c81..d6e79cca 100644 --- a/doc/plm/lplr.qmd +++ b/doc/plm/lplr.qmd @@ -64,7 +64,6 @@ generate_and_show_styled_table( display_cols=display_columns_coverage, n_rep=n_rep_coverage, level_col="level", -# rename_map={"Learner g": "Learner l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -78,7 +77,6 @@ generate_and_show_styled_table( display_cols=display_columns_coverage, n_rep=n_rep_coverage, level_col="level", -# rename_map={"Learner g": "Learner l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -132,16 +130,16 @@ print(metadata_df.T.to_string(header=False)) #| echo: false # set up data and rename columns -df_coverage = pd.read_csv("../../results/plm/lplr_ate_tune_coverage.csv", index_col=None) +df_coverage_tune = pd.read_csv("../../results/plm/lplr_ate_tune_coverage.csv", index_col=None) -if "repetition" in df_coverage.columns and df_coverage["repetition"].nunique() == 1: - n_rep_coverage = df_coverage["repetition"].unique()[0] -elif "n_rep" in df_coverage.columns and df_coverage["n_rep"].nunique() == 1: - n_rep_coverage = df_coverage["n_rep"].unique()[0] +if "repetition" in df_coverage_tune.columns and df_coverage_tune["repetition"].nunique() == 1: + n_rep_coverage_tune = df_coverage_tune["repetition"].unique()[0] +elif "n_rep" in df_coverage_tune.columns and df_coverage_tune["n_rep"].nunique() == 1: + n_rep_coverage_tune = df_coverage_tune["n_rep"].unique()[0] else: - n_rep_coverage = "N/A" # Fallback if n_rep cannot be determined + n_rep_coverage_tune = "N/A" # Fallback if n_rep cannot be determined -display_columns_coverage = ["Learner m", "Learner M", "Learner t", "Tuned", "Bias", "CI Length", "Coverage"] +display_columns_coverage_tune = ["Learner m", "Learner M", "Learner t", "Tuned", "Bias", "CI Length", "Coverage", "Loss M", "Loss a", "Loss m"] ``` ### Nuisance space @@ -150,12 +148,11 @@ display_columns_coverage = ["Learner m", "Learner M", "Learner t", "Tuned", "Bia # | echo: false generate_and_show_styled_table( - main_df=df_coverage, + main_df=df_coverage_tune, filters={"level": 0.95, "Score": "nuisance_space"}, - display_cols=display_columns_coverage, - n_rep=n_rep_coverage, + display_cols=display_columns_coverage_tune, + n_rep=n_rep_coverage_tune, level_col="level", -# rename_map={"Learner g": "Learner l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -164,12 +161,11 @@ generate_and_show_styled_table( #| echo: false generate_and_show_styled_table( - main_df=df_coverage, + main_df=df_coverage_tune, filters={"level": 0.9, "Score": "nuisance_space"}, - display_cols=display_columns_coverage, - n_rep=n_rep_coverage, + display_cols=display_columns_coverage_tune, + n_rep=n_rep_coverage_tune, level_col="level", -# rename_map={"Learner g": "Learner l"}, coverage_highlight_cols=["Coverage"] ) ``` @@ -181,10 +177,10 @@ generate_and_show_styled_table( #| echo: false generate_and_show_styled_table( - main_df=df_coverage, + main_df=df_coverage_tune, filters={"level": 0.95, "Score": "instrument"}, - display_cols=display_columns_coverage, - n_rep=n_rep_coverage, + display_cols=display_columns_coverage_tune, + n_rep=n_rep_coverage_tune, level_col="level", coverage_highlight_cols=["Coverage"] ) @@ -194,10 +190,10 @@ generate_and_show_styled_table( #| echo: false generate_and_show_styled_table( - main_df=df_coverage, + main_df=df_coverage_tune, filters={"level": 0.9, "Score": "instrument"}, - display_cols=display_columns_coverage, - n_rep=n_rep_coverage, + display_cols=display_columns_coverage_tune, + n_rep=n_rep_coverage_tune, level_col="level", coverage_highlight_cols=["Coverage"] ) diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py index 63c4acef..82672e5a 100644 --- a/monte-cover/src/montecover/did/did_pa_multi_tune.py +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -8,6 +8,7 @@ from montecover.base import BaseSimulation from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params, lgbm_cls_params class DIDMultiTuningCoverageSimulation(BaseSimulation): @@ -34,29 +35,7 @@ def __init__( self._calculate_oracle_values() # tuning specific settings - # parameter space for the outcome regression tuning - def ml_g_params(trial): - return { - 'n_estimators': 100, - 'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True), - 'max_depth': 3, - 'min_child_samples': trial.suggest_int('min_child_samples', 10, 20, step=5), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-2, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-2, 10.0, log=True), - } - - # parameter space for the propensity score tuning - def ml_m_params(trial): - return { - 'n_estimators': 100, - 'learning_rate': trial.suggest_float('learning_rate', 0.001, 0.1, log=True), - 'max_depth': 3, - 'min_child_samples': trial.suggest_int('min_child_samples', 10, 20, step=5), - 'lambda_l1': trial.suggest_float('lambda_l1', 1e-2, 10.0, log=True), - 'lambda_l2': trial.suggest_float('lambda_l2', 1e-2, 10.0, log=True), - } - - self._param_space = {"ml_g": ml_g_params, "ml_m": ml_m_params} + self._param_space = {"ml_g": lgbm_reg_params, "ml_m": lgbm_cls_params} self._optuna_settings = { "n_trials": 50, @@ -67,7 +46,6 @@ def ml_m_params(trial): def _process_config_parameters(self): """Process simulation-specific parameters from config""" # Process ML models in parameter grid - # Process ML models in parameter grid assert ( "learners" in self.dml_parameters ), "No learners specified in the config file" @@ -156,6 +134,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: for model in [dml_model, dml_model_tuned]: model.fit() model.bootstrap(n_rep_boot=2000) + nuisance_loss = model.nuisance_loss for level in self.confidence_parameters["level"]: level_result = dict() level_result["detailed"] = self._compute_coverage( @@ -188,6 +167,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "In-sample-norm.": in_sample_normalization, "level": level, "Tuned": model is dml_model_tuned, + "Loss g_control": nuisance_loss["ml_g0"].mean(), + "Loss g_treated": nuisance_loss["ml_g1"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -214,6 +196,9 @@ def summarize_results(self): "Bias": "mean", "Uniform Coverage": "mean", "Uniform CI Length": "mean", + "Loss g_control": "mean", + "Loss g_treated": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/monte-cover/src/montecover/plm/lplr_ate_tune.py b/monte-cover/src/montecover/plm/lplr_ate_tune.py index 994f7e35..1e25fce5 100644 --- a/monte-cover/src/montecover/plm/lplr_ate_tune.py +++ b/monte-cover/src/montecover/plm/lplr_ate_tune.py @@ -6,6 +6,7 @@ from montecover.base import BaseSimulation from montecover.utils import create_learner_from_config +from montecover.utils_tuning import lgbm_reg_params, lgbm_cls_params class LPLRATETuningCoverageSimulation(BaseSimulation): @@ -30,26 +31,12 @@ def __init__( self._calculate_oracle_values() self._use_failed_scores = use_failed_scores - # for simplicity, we use the same parameter space for all learners - def ml_params(trial): - return { - "n_estimators": trial.suggest_int("n_estimators", 100, 500, step=50), - "learning_rate": trial.suggest_float( - "learning_rate", 1e-3, 0.1, log=True - ), - "min_child_samples": trial.suggest_int( - "min_child_samples", 20, 100, step=5 - ), - "max_depth": trial.suggest_int("max_depth", 3, 10, step=1), - "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True), - "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True), - } - + # tuning specific settings self._param_space = { - "ml_M": ml_params, - "ml_t": ml_params, - "ml_m": ml_params, - "ml_a": ml_params, + "ml_M": lgbm_cls_params, + "ml_t": lgbm_reg_params, + "ml_m": lgbm_reg_params, + "ml_a": lgbm_reg_params, } self._optuna_settings = { @@ -112,7 +99,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: except RuntimeError as e: self.logger.info(f"Exception during fit: {e}") return None - + nuisance_loss = model.nuisance_loss for level in self.confidence_parameters["level"]: level_result = dict() level_result["coverage"] = self._compute_coverage( @@ -132,6 +119,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Score": score, "level": level, "Tuned": model is dml_model_tuned, + "Loss M": nuisance_loss["ml_M"].mean(), + "Loss a": nuisance_loss["ml_a"].mean(), + "Loss m": nuisance_loss["ml_m"].mean(), } ) for key, res in level_result.items(): @@ -156,6 +146,9 @@ def summarize_results(self): "Coverage": "mean", "CI Length": "mean", "Bias": "mean", + "Loss M": "mean", + "Loss a": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/results/plm/lplr_ate_tune_coverage.csv b/results/plm/lplr_ate_tune_coverage.csv index 8a2c6bf1..48db3e7c 100644 --- a/results/plm/lplr_ate_tune_coverage.csv +++ b/results/plm/lplr_ate_tune_coverage.csv @@ -1,9 +1,9 @@ -Learner m,Learner M,Learner t,Score,level,Tuned,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,False,0.91,0.9117258212067718,0.240354871477558,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,True,0.95,0.8692681775643711,0.2054770002796413,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,False,0.98,1.0863883229855305,0.240354871477558,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,True,0.96,1.0357969201737371,0.2054770002796413,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,False,0.91,0.7841573908306078,0.18430486050109982,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,True,0.86,0.7221800622589235,0.1665060542122647,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,False,0.95,0.9343811625885382,0.18430486050109982,100 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,True,0.93,0.8605306205900738,0.1665060542122647,100 +Learner m,Learner M,Learner t,Score,level,Tuned,Coverage,CI Length,Bias,Loss M,Loss a,Loss m,repetition +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,False,0.89,0.9503738196199937,0.27958957834627335,0.7313248611019764,0.3524379916385648,0.3761979381237934,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,True,0.86,0.7888791069378829,0.2131544006122909,0.6305442473961151,0.33110728634697295,0.335336012157393,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,False,0.92,1.1324402535180162,0.27958957834627335,0.7313248611019764,0.3524379916385648,0.3761979381237934,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,True,0.92,0.9400074343514756,0.2131544006122909,0.6305442473961151,0.33110728634697295,0.335336012157393,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,False,0.89,0.8105540967664803,0.19761923280521046,0.7266071266302078,0.35476756729034437,0.479148010505746,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,True,0.87,0.6815753718036465,0.20220182977401963,0.6305431158253273,0.3305870433409719,0.46869360133810006,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,False,0.94,0.9658347777291706,0.19761923280521046,0.7266071266302078,0.35476756729034437,0.479148010505746,100 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,True,0.91,0.8121471476829805,0.20220182977401963,0.6305431158253273,0.3305870433409719,0.46869360133810006,100 diff --git a/results/plm/lplr_ate_tune_metadata.csv b/results/plm/lplr_ate_tune_metadata.csv index 7b432298..17c16674 100644 --- a/results/plm/lplr_ate_tune_metadata.csv +++ b/results/plm/lplr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,LPLRATETuningCoverageSimulation,2025-11-26 17:47,44.12576818863551,3.12.9,scripts/plm/lplr_ate_tune_config.yml +0.12.dev0,LPLRATETuningCoverageSimulation,2025-12-01 14:39,38.92375212907791,3.12.9,scripts/plm/lplr_ate_tune_config.yml From 10bb9d9011a9153eb22ccc1bbefef27addcbb558 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 16:43:45 +0100 Subject: [PATCH 21/51] Update DID multi-tuning results and metadata --- doc/did/did_pa_multi.qmd | 68 +++++++++++--------- results/did/did_pa_multi_tune_detailed.csv | 34 +++++----- results/did/did_pa_multi_tune_eventstudy.csv | 34 +++++----- results/did/did_pa_multi_tune_group.csv | 34 +++++----- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 34 +++++----- 6 files changed, 106 insertions(+), 100 deletions(-) diff --git a/doc/did/did_pa_multi.qmd b/doc/did/did_pa_multi.qmd index 4270ac8a..823cc5b3 100644 --- a/doc/did/did_pa_multi.qmd +++ b/doc/did/did_pa_multi.qmd @@ -326,6 +326,9 @@ generate_and_show_styled_table( The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Due to time constraints we only consider one learner, use in-sample normalization and the following DGPs: + - Type 1: Linear outcome model and treatment assignment + - Type 2: Nonlinear outcome model and linear treatment assignment + - Type 3: Linear outcome model and nonlinear treatment assignment - Type 4: Nonlinear outcome model and treatment assignment The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. @@ -350,7 +353,7 @@ df = pd.read_csv("../../results/did/did_pa_multi_tune_detailed.csv", index_col=N assert df["repetition"].nunique() == 1 n_rep = df["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` ### Observational Score @@ -385,6 +388,9 @@ These simulations test different types of aggregation, as described in [DiD User As before, we only consider one learner, use in-sample normalization and the following DGPs: + - Type 1: Linear outcome model and treatment assignment + - Type 2: Nonlinear outcome model and linear treatment assignment + - Type 3: Linear outcome model and nonlinear treatment assignment - Type 4: Nonlinear outcome model and treatment assignment The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. @@ -395,12 +401,12 @@ The non-uniform results (coverage, ci length and bias) refer to averaged values #| echo: false # set up data -df_group = pd.read_csv("../../results/did/did_pa_multi_tune_group.csv", index_col=None) +df_group_tune = pd.read_csv("../../results/did/did_pa_multi_tune_group.csv", index_col=None) -assert df_group["repetition"].nunique() == 1 -n_rep_group = df_group["repetition"].unique()[0] +assert df_group_tune["repetition"].nunique() == 1 +n_rep_group_tune = df_group_tune["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns_tune = ["Learner g", "Learner m", "DGP", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -408,10 +414,10 @@ display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Cove ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_group, + main_df=df_group_tune, filters={"level": 0.95, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_group, + display_cols=display_columns_tune, + n_rep=n_rep_group_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -420,10 +426,10 @@ generate_and_show_styled_table( ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_group, + main_df=df_group_tune, filters={"level": 0.9, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_group, + display_cols=display_columns_tune, + n_rep=n_rep_group_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -436,12 +442,12 @@ generate_and_show_styled_table( #| echo: false # set up data -df_time = pd.read_csv("../../results/did/did_pa_multi_tune_time.csv", index_col=None) +df_time_tune = pd.read_csv("../../results/did/did_pa_multi_tune_time.csv", index_col=None) -assert df_time["repetition"].nunique() == 1 -n_rep_time = df_time["repetition"].unique()[0] +assert df_time_tune["repetition"].nunique() == 1 +n_rep_time_tune = df_time_tune["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns_tune = ["Learner g", "Learner m", "DGP", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -449,10 +455,10 @@ display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Cove ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_time, + main_df=df_time_tune, filters={"level": 0.95, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_time, + display_cols=display_columns_tune, + n_rep=n_rep_time_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -461,10 +467,10 @@ generate_and_show_styled_table( ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_time, + main_df=df_time_tune, filters={"level": 0.9, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_time, + display_cols=display_columns_tune, + n_rep=n_rep_time_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -476,12 +482,12 @@ generate_and_show_styled_table( #| echo: false # set up data -df_es = pd.read_csv("../../results/did/did_pa_multi_tune_eventstudy.csv", index_col=None) +df_es_tune = pd.read_csv("../../results/did/did_pa_multi_tune_eventstudy.csv", index_col=None) -assert df_es["repetition"].nunique() == 1 -n_rep_es = df_es["repetition"].unique()[0] +assert df_es_tune["repetition"].nunique() == 1 +n_rep_es_tune = df_es_tune["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns_tune = ["Learner g", "Learner m", "DGP", "Tuned", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -489,10 +495,10 @@ display_columns = ["Learner g", "Learner m", "Tuned", "Bias", "CI Length", "Cove ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_es, + main_df=df_es_tune, filters={"level": 0.95, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_es, + display_cols=display_columns_tune, + n_rep=n_rep_es_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) @@ -501,10 +507,10 @@ generate_and_show_styled_table( ```{python} #| echo: false generate_and_show_styled_table( - main_df=df_es, + main_df=df_es_tune, filters={"level": 0.9, "Score": "observational"}, - display_cols=display_columns, - n_rep=n_rep_es, + display_cols=display_columns_tune, + n_rep=n_rep_es_tune, level_col="level", coverage_highlight_cols=["Coverage", "Uniform Coverage"] ) diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index c0361bba..c1791c65 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,17 +1,17 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9041666666666667,1.1400890857194432,0.28740542972213634,0.92,1.7825586546503152,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8966666666666667,0.6472337011214075,0.15944046942151935,0.89,1.0113932407840978,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9608333333333333,1.3584999361424834,0.28740542972213634,0.98,1.9530265671281697,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9516666666666667,0.7712265231342353,0.15944046942151935,0.94,1.108993599863019,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9091666666666667,1.228317976026286,0.29714569696579457,0.87,1.9154225471875832,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.9075,0.6999396898634899,0.16414917750405422,0.89,1.0923185146373655,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9625,1.4636311432990996,0.29714569696579457,0.96,2.1000879769164107,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.955,0.8340295823313698,0.16414917750405422,0.92,1.1991852306848672,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9116666666666667,1.3107305270692071,0.31985432471813957,0.93,2.043307297150825,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8908333333333333,0.7112998258902837,0.17517537461692242,0.84,1.1042718075016527,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9616666666666667,1.5618317547526317,0.31985432471813957,0.96,2.2409152941225887,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9391666666666667,0.8475660193171083,0.17517537461692242,0.9,1.2154039059944974,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9166666666666667,1.4374818316154423,0.33276716041579246,0.94,2.2272959804052928,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7691666666666667,0.7404367767653766,0.254447961458634,0.69,1.1489525496687707,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9675,1.712865249668844,0.33276716041579246,0.96,2.449349686911489,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8541666666666667,0.8822848376963073,0.254447961458634,0.79,1.2629079128831524,100 +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8991666666666667,1.1367782872857282,0.2762362295842985,0.93,1.7739576901791148,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8775,0.6396360988823492,0.1679386739873294,0.83,1.0011531338160695,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9625,1.354554876482567,0.2762362295842985,0.97,1.9460309141153076,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9233333333333333,0.7621734216828837,0.1679386739873294,0.89,1.0961658049458993,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.8983333333333333,1.2330356378884049,0.30663270016192556,0.92,1.9173958386413434,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.8916666666666667,0.6719441811647116,0.16476409918696738,0.86,1.0513367602608807,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9541666666666667,1.4692525841309676,0.30663270016192556,0.96,2.1053464077111452,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9425,0.8006708761952025,0.16476409918696738,0.93,1.1526329365873031,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.905,1.31358053465385,0.32661174597213943,0.9,2.0440203617896806,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.865,0.7289212442976212,0.1945603364540163,0.86,1.1371526826681673,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9566666666666667,1.5652277482501924,0.32661174597213943,0.96,2.242653923089107,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9308333333333333,0.8685632344303481,0.1945603364540163,0.88,1.2462114970320124,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9258333333333333,1.4012822649889498,0.33623310300936715,0.91,2.1760953165140573,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.775057031885869,0.27368317437679723,0.6,1.2076437086430896,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.965,1.6697308055570117,0.33623310300936715,0.94,2.3932554225940295,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8358333333333333,0.9235374160777122,0.27368317437679723,0.71,1.325967981957511,2.996299502921045,3.0408206631075583,0.6757679707228056,100 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index d6f6b58d..ade36ad8 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,17 +1,17 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9283333333333332,1.073068168296971,0.2597502402941499,0.93,1.4984680319815546,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.905,0.5982631043246142,0.14657170491101748,0.88,0.8348004786911729,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9733333333333333,1.2786395873512446,0.2597502402941499,0.98,1.677549043525822,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9516666666666667,0.7128744579714308,0.14657170491101748,0.97,0.9341199957423918,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9183333333333333,1.184163621831536,0.28342610855647676,0.94,1.6513528430347295,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.895,0.6576942435782439,0.15332741525347127,0.9,0.9110830194425428,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9616666666666667,1.4110179851649454,0.28342610855647676,0.99,1.8462857744217072,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.95,0.7836910282660079,0.15332741525347127,0.95,1.0193279652523601,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9183333333333333,1.2647923551028497,0.2906734347752842,0.95,1.7525858381045432,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.885,0.6743634497620673,0.16589326251977687,0.88,0.9307320111152868,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9683333333333333,1.5070930466424515,0.2906734347752842,0.97,1.9677774556587133,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9383333333333332,0.8035536125323768,0.16589326251977687,0.93,1.0446188993963292,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9283333333333332,1.4064992969714387,0.314613746630164,0.91,1.9372254520475036,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7133333333333333,0.7088318207664741,0.26929824646603895,0.66,0.975482632053878,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9683333333333333,1.6759472825883586,0.314613746630164,0.98,2.1745712334757688,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8033333333333332,0.8446252098267877,0.26929824646603895,0.76,1.0954531498790554,100 +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9083333333333333,1.0742547796394701,0.2618988136569734,0.94,1.5022639132232276,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8633333333333333,0.5878107921893911,0.15731265501369246,0.88,0.8222540013002478,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9566666666666667,1.2800535219754794,0.2618988136569734,0.98,1.6776898490222303,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9233333333333333,0.7004197598727457,0.15731265501369246,0.91,0.915822574094016,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9016666666666667,1.1885817333189461,0.28095848380634825,0.9,1.6488463476730966,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.91,0.6233279876475303,0.1499301158544056,0.91,0.8702171833507445,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9533333333333333,1.4162824897099824,0.28095848380634825,0.95,1.844474784460219,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9533333333333333,0.7427411085868194,0.1499301158544056,0.94,0.9737952014197065,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.91,1.265646440169177,0.3020810902480612,0.96,1.7582545006922015,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8516666666666667,0.6864549574097271,0.18550205150287405,0.82,0.9531749334821116,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9566666666666667,1.5081107517697077,0.3020810902480612,0.99,1.9733460594562338,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.91,0.8179615325563163,0.18550205150287405,0.91,1.0675220384475739,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.3639352077948335,0.31325283477445254,0.92,1.8864279971533038,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.675,0.7467959521377197,0.29502185426592087,0.57,1.0327936903874404,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.955,1.6252290420993776,0.31325283477445254,0.94,2.1144079580645485,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.76,0.8898622625181539,0.29502185426592087,0.71,1.157187445369494,2.996299502921045,3.0408206631075583,0.6757679707228056,100 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index a36433f0..4554ea06 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,17 +1,17 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8933333333333333,1.104533138085938,0.2786392828405217,0.93,1.403347738672554,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.9166666666666667,0.6434665238707676,0.15552856896641956,0.91,0.8209959260997447,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9666666666666667,1.3161324113633812,0.2786392828405217,0.97,1.5959470400469875,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9533333333333333,0.7667376545725277,0.15552856896641956,0.96,0.9301867858119841,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.92,1.2001857652221994,0.2761379784298203,0.94,1.5248040252023016,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.91,0.705320543228418,0.15481408501446786,0.88,0.8960249286786839,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.96,1.430109546557577,0.2761379784298203,0.97,1.7238479263548212,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.95,0.8404412645798971,0.15481408501446786,0.93,1.0186697969238927,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.8933333333333333,1.3085128545125806,0.30695508841663705,0.92,1.6601995824415863,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.89,0.7237290131511886,0.16938601624368954,0.91,0.9191510186073212,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9533333333333333,1.5591892349142273,0.30695508841663705,0.96,1.8814562718978545,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9533333333333333,0.8623763094178922,0.16938601624368954,0.92,1.042629687574485,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.4575276036298743,0.3136429990461747,0.93,1.841601446334188,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.7563191729586094,0.28371438189486503,0.66,0.9629096996466265,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9733333333333333,1.7367512602820832,0.3136429990461747,0.97,2.0888217668673956,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8233333333333333,0.9012098800325217,0.28371438189486503,0.81,1.087106451467499,100 +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9333333333333332,1.1085497176172174,0.25053137815681464,0.97,1.4092013919200317,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.88,0.6270768096547109,0.16475748288364936,0.86,0.7990834496186516,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9766666666666667,1.3209184610722169,0.25053137815681464,0.99,1.594604589934972,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.92,0.747208105526932,0.16475748288364936,0.92,0.904463289343679,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.91,1.2178833587850213,0.2840522548466761,0.93,1.545309551595824,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.89,0.6634109544707237,0.1557070964325136,0.95,0.8467228385738873,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9666666666666667,1.4511975299670465,0.2840522548466761,0.98,1.7560977865592113,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9566666666666667,0.7905029094423619,0.1557070964325136,0.97,0.9593452409062102,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9266666666666667,1.3054382973147876,0.31275604791627104,0.92,1.6577767591094739,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8733333333333333,0.7327385795188934,0.19959731742987738,0.85,0.9301408865604531,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9666666666666667,1.5555256740493915,0.31275604791627104,0.95,1.8751718957945658,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9266666666666667,0.8731118699004089,0.19959731742987738,0.92,1.0542511457228387,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.3901952872075152,0.3085413587438872,0.94,1.7625009703571424,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6833333333333332,0.7884798862014211,0.29872708825391375,0.65,1.0051398890297463,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9666666666666667,1.6565198566963024,0.3085413587438872,0.98,1.9995158900115932,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7733333333333333,0.939531733503372,0.29872708825391375,0.78,1.1340404414042033,2.996299502921045,3.0408206631075583,0.6757679707228056,100 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index a62f187f..18110f07 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 10:11,45.433327130476634,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 16:19,97.41892901659011,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index e9325fbb..1ef46733 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,17 +1,17 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9,1.1113763663388696,0.2783388170117924,0.92,1.3788558172615877,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8833333333333333,0.6148427992241794,0.15024436835827917,0.91,0.7634587423773123,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.96,1.3242866207677713,0.2783388170117924,0.97,1.5683457246716077,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.95,0.732630382964001,0.15024436835827917,0.99,0.8733603074599771,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.93,1.2340740537756638,0.2827466455736652,0.94,1.5228025759168033,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.93,0.6909121668851053,0.14601582394245094,0.91,0.8519331823086561,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.97,1.470489932978703,0.2827466455736652,0.97,1.7440031028399017,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9533333333333333,0.8232726252275122,0.14601582394245094,0.97,0.9758565772068002,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9433333333333332,1.2959603630389032,0.2926620156379758,0.96,1.6022423636178649,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8933333333333333,0.7034275560361475,0.16697163725248976,0.88,0.8670910640659377,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.98,1.5442320187817185,0.2926620156379758,0.98,1.834795211566282,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9533333333333333,0.8381856312157769,0.16697163725248976,0.96,0.9914556000230386,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9366666666666668,1.4986674065988734,0.3275028156163638,0.95,1.8415020458286135,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.67,0.7464049203833408,0.300651425058323,0.58,0.9150101228510614,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9833333333333333,1.785772359077211,0.3275028156163638,0.96,2.1067443351244504,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7733333333333333,0.8893963194440494,0.300651425058323,0.67,1.046760454668414,100 +Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9166666666666667,1.1180153505443646,0.2606682372310071,0.93,1.3864882281480229,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8733333333333333,0.6030574071725763,0.1565946675844815,0.84,0.7528737860644216,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9666666666666667,1.3321974583787854,0.2606682372310071,0.96,1.5789327743129655,3.661560613833878,2.9515587152603526,0.8541707478068831,100 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9133333333333333,0.7185872221706374,0.1565946675844815,0.91,0.8562340803893964,3.360158161171733,2.7665447739373614,0.6798065307413697,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9133333333333333,1.242495846865,0.2876013124031499,0.91,1.5298165317768868,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.9033333333333333,0.6448301092139712,0.15700850688300302,0.9,0.8014900549720232,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9666666666666667,1.4805251184018218,0.2876013124031499,0.99,1.749453245020974,3.386477693258062,3.152253900679997,0.8480386768604923,100 +LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.94,0.7683624667252537,0.15700850688300302,0.95,0.9159606719703284,3.1088298411208632,2.92912769411877,0.6763092050163523,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9133333333333333,1.3026045468345968,0.3097734464824378,0.92,1.6060493723360418,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.86,0.7122560459148888,0.19114797442163362,0.89,0.8818745703298427,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9566666666666667,1.5521490520865964,0.3097734464824378,0.98,1.8349776251741563,2.999802652037201,2.982841611043071,0.8507272462648376,100 +LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.94,0.8487054257535311,0.19114797442163362,0.9,1.0053534162566609,3.032548727546919,3.0240798464809977,0.6800265869273724,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.93,1.4429944592183708,0.3314766518697931,0.92,1.7744981673667102,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.66,0.7879531552150065,0.3147134048784779,0.62,0.9681678309328025,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9633333333333333,1.7194339506066572,0.3314766518697931,0.94,2.0249437616646766,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.76,0.9389040948211221,0.3147134048784779,0.68,1.107460648539683,2.996299502921045,3.0408206631075583,0.6757679707228056,100 From 84ea55f6d3d3d87707482619d4f1ec92da0e6600 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Mon, 1 Dec 2025 21:58:39 +0100 Subject: [PATCH 22/51] add did --- .../src/montecover/did/did_pa_multi_tune.py | 5 ++++ results/did/did_pa_multi_tune_config.yml | 6 ++--- results/did/did_pa_multi_tune_detailed.csv | 26 +++++++------------ results/did/did_pa_multi_tune_eventstudy.csv | 26 +++++++------------ results/did/did_pa_multi_tune_group.csv | 26 +++++++------------ results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 26 +++++++------------ scripts/did/did_pa_multi_tune_config.yml | 7 +++-- 8 files changed, 50 insertions(+), 74 deletions(-) diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py index 82672e5a..c9a3249a 100644 --- a/monte-cover/src/montecover/did/did_pa_multi_tune.py +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -91,6 +91,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: learner_g_name, ml_g = create_learner_from_config(learner_config["ml_g"]) learner_m_name, ml_m = create_learner_from_config(learner_config["ml_m"]) score = dml_params["score"] + control_group = dml_params["control_group"] in_sample_normalization = dml_params["in_sample_normalization"] # Model @@ -100,6 +101,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ml_m=None if score == "experimental" else ml_m, gt_combinations="standard", score=score, + control_group=control_group, in_sample_normalization=in_sample_normalization, ) # Tuning @@ -109,6 +111,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ml_m=None if score == "experimental" else ml_m, gt_combinations="standard", score=score, + control_group=control_group, in_sample_normalization=in_sample_normalization, ) dml_model_tuned.tune_ml_models( @@ -164,6 +167,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Learner g": learner_g_name, "Learner m": learner_m_name, "Score": score, + "Control Group": control_group, "In-sample-norm.": in_sample_normalization, "level": level, "Tuned": model is dml_model_tuned, @@ -185,6 +189,7 @@ def summarize_results(self): "Learner g", "Learner m", "Score", + "Control Group", "In-sample-norm.", "DGP", "level", diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml index 0bcf1119..fbf8f91b 100644 --- a/results/did/did_pa_multi_tune_config.yml +++ b/results/did/did_pa_multi_tune_config.yml @@ -1,13 +1,11 @@ simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 random_seed: 42 n_jobs: -2 dgp_parameters: DGP: - 1 - - 2 - - 3 - 4 n_obs: - 2000 @@ -20,6 +18,8 @@ dml_parameters: learners: - ml_g: *id001 ml_m: *id002 + control_group: + - never_treated score: - observational in_sample_normalization: diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index c1791c65..59641769 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,17 +1,9 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.8991666666666667,1.1367782872857282,0.2762362295842985,0.93,1.7739576901791148,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8775,0.6396360988823492,0.1679386739873294,0.83,1.0011531338160695,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9625,1.354554876482567,0.2762362295842985,0.97,1.9460309141153076,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9233333333333333,0.7621734216828837,0.1679386739873294,0.89,1.0961658049458993,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.8983333333333333,1.2330356378884049,0.30663270016192556,0.92,1.9173958386413434,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.8916666666666667,0.6719441811647116,0.16476409918696738,0.86,1.0513367602608807,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9541666666666667,1.4692525841309676,0.30663270016192556,0.96,2.1053464077111452,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9425,0.8006708761952025,0.16476409918696738,0.93,1.1526329365873031,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.905,1.31358053465385,0.32661174597213943,0.9,2.0440203617896806,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.865,0.7289212442976212,0.1945603364540163,0.86,1.1371526826681673,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9566666666666667,1.5652277482501924,0.32661174597213943,0.96,2.242653923089107,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9308333333333333,0.8685632344303481,0.1945603364540163,0.88,1.2462114970320124,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9258333333333333,1.4012822649889498,0.33623310300936715,0.91,2.1760953165140573,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.7533333333333333,0.775057031885869,0.27368317437679723,0.6,1.2076437086430896,2.996299502921045,3.0408206631075583,0.6757679707228056,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.965,1.6697308055570117,0.33623310300936715,0.94,2.3932554225940295,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.8358333333333333,0.9235374160777122,0.27368317437679723,0.71,1.325967981957511,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9120833333333332,1.1281458608527684,0.27675088039547086,0.94,1.766959072388302,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.89375,0.6376532313206807,0.15286697800217827,0.915,0.9983993286704501,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9620833333333333,1.3442687059500862,0.27675088039547086,0.97,1.9367883375747112,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM 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Clas.,observational,never_treated,True,4,0.95,True,0.8429166666666668,0.926475840337015,0.2683054004360395,0.775,1.3270667190666499,2.989557883560226,3.044965814893618,0.6749671427573682,200 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index ade36ad8..31abae9c 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,17 +1,9 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9083333333333333,1.0742547796394701,0.2618988136569734,0.94,1.5022639132232276,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM 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Clas.,observational,True,3,0.95,True,0.91,0.8179615325563163,0.18550205150287405,0.91,1.0675220384475739,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.3639352077948335,0.31325283477445254,0.92,1.8864279971533038,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.675,0.7467959521377197,0.29502185426592087,0.57,1.0327936903874404,2.996299502921045,3.0408206631075583,0.6757679707228056,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.955,1.6252290420993776,0.31325283477445254,0.94,2.1144079580645485,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.76,0.8898622625181539,0.29502185426592087,0.71,1.157187445369494,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9075,1.044931024932891,0.24865990464197016,0.935,1.4671727358061626,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.8933333333333333,0.5855097565227854,0.13924209096865323,0.925,0.8214814724321132,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9583333333333333,1.245112113101973,0.24865990464197016,0.985,1.6330868182303546,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9508333333333333,0.6976779067620538,0.13924209096865323,0.945,0.9148690368152829,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.92,1.4033187650033796,0.3275689371006418,0.94,1.937780599061532,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.705,0.7446092869675938,0.27966561432340287,0.6,1.0285584537514965,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9683333333333333,1.6721574449961667,0.3275689371006418,0.975,2.1663251795251472,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.7883333333333333,0.8872566902596436,0.27966561432340287,0.745,1.153279573853566,2.989557883560226,3.044965814893618,0.6749671427573682,200 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index 4554ea06..5ee880ab 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,17 +1,9 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9333333333333332,1.1085497176172174,0.25053137815681464,0.97,1.4092013919200317,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.88,0.6270768096547109,0.16475748288364936,0.86,0.7990834496186516,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9766666666666667,1.3209184610722169,0.25053137815681464,0.99,1.594604589934972,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.92,0.747208105526932,0.16475748288364936,0.92,0.904463289343679,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.91,1.2178833587850213,0.2840522548466761,0.93,1.545309551595824,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.89,0.6634109544707237,0.1557070964325136,0.95,0.8467228385738873,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9666666666666667,1.4511975299670465,0.2840522548466761,0.98,1.7560977865592113,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.9566666666666667,0.7905029094423619,0.1557070964325136,0.97,0.9593452409062102,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9266666666666667,1.3054382973147876,0.31275604791627104,0.92,1.6577767591094739,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.8733333333333333,0.7327385795188934,0.19959731742987738,0.85,0.9301408865604531,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9666666666666667,1.5555256740493915,0.31275604791627104,0.95,1.8751718957945658,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.9266666666666667,0.8731118699004089,0.19959731742987738,0.92,1.0542511457228387,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.9233333333333333,1.3901952872075152,0.3085413587438872,0.94,1.7625009703571424,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.6833333333333332,0.7884798862014211,0.29872708825391375,0.65,1.0051398890297463,2.996299502921045,3.0408206631075583,0.6757679707228056,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9666666666666667,1.6565198566963024,0.3085413587438872,0.98,1.9995158900115932,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.7733333333333333,0.939531733503372,0.29872708825391375,0.78,1.1340404414042033,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9116666666666667,1.0889543359860974,0.2591017535241754,0.91,1.3866236446708151,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.89,0.6229752808903669,0.15053905583073077,0.905,0.7933914451788466,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9566666666666667,1.2975691237020013,0.2591017535241754,0.97,1.5696656784049068,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9533333333333333,0.7423208325635811,0.15053905583073077,0.96,0.8996786017154156,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9133333333333333,1.4454910752551433,0.3253154503600127,0.96,1.8286646948756324,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.74,0.7892476267585741,0.28546431334778133,0.645,1.0046266703621765,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9616666666666667,1.7224088521025238,0.3253154503600127,0.99,2.07498069683797,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.825,0.9404465528020834,0.28546431334778133,0.77,1.139020606132637,2.989557883560226,3.044965814893618,0.6749671427573682,200 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index 18110f07..89449231 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 16:19,97.41892901659011,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 21:52,119.05648648738861,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index 1ef46733..2f055ded 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,17 +1,9 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,False,0.9166666666666667,1.1180153505443646,0.2606682372310071,0.93,1.3864882281480229,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,True,0.8733333333333333,0.6030574071725763,0.1565946675844815,0.84,0.7528737860644216,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,False,0.9666666666666667,1.3321974583787854,0.2606682372310071,0.96,1.5789327743129655,3.661560613833878,2.9515587152603526,0.8541707478068831,100 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,True,0.9133333333333333,0.7185872221706374,0.1565946675844815,0.91,0.8562340803893964,3.360158161171733,2.7665447739373614,0.6798065307413697,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,False,0.9133333333333333,1.242495846865,0.2876013124031499,0.91,1.5298165317768868,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.9,True,0.9033333333333333,0.6448301092139712,0.15700850688300302,0.9,0.8014900549720232,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,False,0.9666666666666667,1.4805251184018218,0.2876013124031499,0.99,1.749453245020974,3.386477693258062,3.152253900679997,0.8480386768604923,100 -LGBM Regr.,LGBM Clas.,observational,True,2,0.95,True,0.94,0.7683624667252537,0.15700850688300302,0.95,0.9159606719703284,3.1088298411208632,2.92912769411877,0.6763092050163523,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,False,0.9133333333333333,1.3026045468345968,0.3097734464824378,0.92,1.6060493723360418,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.9,True,0.86,0.7122560459148888,0.19114797442163362,0.89,0.8818745703298427,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,False,0.9566666666666667,1.5521490520865964,0.3097734464824378,0.98,1.8349776251741563,2.999802652037201,2.982841611043071,0.8507272462648376,100 -LGBM Regr.,LGBM Clas.,observational,True,3,0.95,True,0.94,0.8487054257535311,0.19114797442163362,0.9,1.0053534162566609,3.032548727546919,3.0240798464809977,0.6800265869273724,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,False,0.93,1.4429944592183708,0.3314766518697931,0.92,1.7744981673667102,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,True,0.66,0.7879531552150065,0.3147134048784779,0.62,0.9681678309328025,2.996299502921045,3.0408206631075583,0.6757679707228056,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,False,0.9633333333333333,1.7194339506066572,0.3314766518697931,0.94,2.0249437616646766,2.9827080885681765,2.9977847813459078,0.8486888442599515,100 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,True,0.76,0.9389040948211221,0.3147134048784779,0.68,1.107460648539683,2.996299502921045,3.0408206631075583,0.6757679707228056,100 +Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9433333333333332,1.0881198449012723,0.25682450851563937,0.945,1.3552240711392805,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.8966666666666667,0.5957046590186345,0.14226747638646786,0.915,0.7443420810052485,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9716666666666667,1.2965747662437588,0.25682450851563937,0.96,1.5447661653225075,3.597108107523503,2.9403917178509804,0.8534357674606872,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9533333333333333,0.7098258823571802,0.14226747638646786,0.955,0.8483239350473682,3.29374008288805,2.748707897836178,0.679591632802887,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9183333333333333,1.4966364514582033,0.3513992824628867,0.94,1.8382232879162173,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.6516666666666667,0.787550236381641,0.30937396820647195,0.6,0.9660154081700578,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9716666666666667,1.7833523267626594,0.3513992824628867,0.965,2.102235052471558,2.979241703960451,2.9994404795169123,0.8446712058892714,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.775,0.9384239874187676,0.30937396820647195,0.705,1.1067012746087066,2.989557883560226,3.044965814893618,0.6749671427573682,200 diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml index b46d6475..9418178d 100644 --- a/scripts/did/did_pa_multi_tune_config.yml +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -1,13 +1,13 @@ # Simulation parameters for DID Multi Coverage simulation_parameters: - repetitions: 100 + repetitions: 200 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: - DGP: [1,2,3,4] # Different DGP specifications + DGP: [1, 4] # Different DGP specifications n_obs: [2000] # Sample size for each simulation (has to be a list) # Define reusable learner configurations @@ -25,6 +25,9 @@ dml_parameters: - ml_g: *lgbmr ml_m: *lgbmc + control_group: + - "never_treated" # Control group specification + score: - observational # Standard DML score From 2cb8aae5faf65a0d5a2adb83a2e9e85c97230e96 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 2 Dec 2025 21:09:20 +0100 Subject: [PATCH 23/51] refactor: update DGP specifications and sample size in configuration files; adjust simulation results accordingly --- .../src/montecover/did/did_pa_multi_tune.py | 3 +-- results/did/did_pa_multi_tune_config.yml | 8 +++++--- results/did/did_pa_multi_tune_detailed.csv | 14 ++++++-------- results/did/did_pa_multi_tune_eventstudy.csv | 14 ++++++-------- results/did/did_pa_multi_tune_group.csv | 14 ++++++-------- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 14 ++++++-------- scripts/did/did_pa_multi_tune_config.yml | 7 ++++--- 8 files changed, 35 insertions(+), 41 deletions(-) diff --git a/monte-cover/src/montecover/did/did_pa_multi_tune.py b/monte-cover/src/montecover/did/did_pa_multi_tune.py index c9a3249a..596566c0 100644 --- a/monte-cover/src/montecover/did/did_pa_multi_tune.py +++ b/monte-cover/src/montecover/did/did_pa_multi_tune.py @@ -33,7 +33,6 @@ def __init__( # Calculate oracle values self._calculate_oracle_values() - # tuning specific settings self._param_space = {"ml_g": lgbm_reg_params, "ml_m": lgbm_cls_params} @@ -218,7 +217,7 @@ def summarize_results(self): def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData: """Generate data for the simulation.""" - data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"]) + data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"], xi=dgp_params.get("xi", 0.0)) dml_data = dml.data.DoubleMLPanelData( data, y_col="y", diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml index fbf8f91b..be727ea3 100644 --- a/results/did/did_pa_multi_tune_config.yml +++ b/results/did/did_pa_multi_tune_config.yml @@ -5,10 +5,13 @@ simulation_parameters: n_jobs: -2 dgp_parameters: DGP: - - 1 + - 2 + - 3 - 4 n_obs: - - 2000 + - 500 + xi: + - 0.2 learner_definitions: lgbmr: &id001 name: LGBM Regr. @@ -26,5 +29,4 @@ dml_parameters: - true confidence_parameters: level: - - 0.95 - 0.9 diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index 59641769..f100f991 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,9 +1,7 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9120833333333332,1.1281458608527684,0.27675088039547086,0.94,1.766959072388302,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.89375,0.6376532313206807,0.15286697800217827,0.915,0.9983993286704501,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9620833333333333,1.3442687059500862,0.27675088039547086,0.97,1.9367883375747112,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9541666666666667,0.759810689252895,0.15286697800217827,0.945,1.0938791918349722,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9041666666666667,1.4389904260432933,0.3468462138226047,0.91,2.2315687188052546,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.7629166666666667,0.7775230352607071,0.2683054004360395,0.63,1.2089929988521952,2.989557883560226,3.044965814893618,0.6749671427573682,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9554166666666667,1.7146628508033268,0.3468462138226047,0.97,2.451356071426852,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.8429166666666668,0.926475840337015,0.2683054004360395,0.775,1.3270667190666499,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.9266666666666667,3.0908848408903418,0.7161526833648638,0.96,4.742031668154074,5.382034194123735,5.231751941184026,0.8576041329426337,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.90125,1.8794931781846032,0.4591493790044287,0.865,2.8579990686983887,4.515059107298566,4.385233448105733,0.6919478275690014,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9308333333333333,2.947927939580188,0.6723441057011548,0.96,4.521838848419626,4.642710293777947,4.646394978665751,0.8553041170789771,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.905,1.8011025121556412,0.4236148425151305,0.885,2.7558988253099552,4.228587388499047,4.190294595950896,0.6911269926300146,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.925,2.9018541428866937,0.6722860434862635,0.94,4.462288809787092,4.612699034697736,4.644364135602212,0.8550565538841952,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8904166666666667,1.7864285303816843,0.4480420728291524,0.88,2.7393757933996197,4.181116753485783,4.208383450296873,0.6911607404321352,200 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index 31abae9c..b603f6f4 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,9 +1,7 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9075,1.044931024932891,0.24865990464197016,0.935,1.4671727358061626,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.8933333333333333,0.5855097565227854,0.13924209096865323,0.925,0.8214814724321132,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9583333333333333,1.245112113101973,0.24865990464197016,0.985,1.6330868182303546,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9508333333333333,0.6976779067620538,0.13924209096865323,0.945,0.9148690368152829,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.92,1.4033187650033796,0.3275689371006418,0.94,1.937780599061532,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.705,0.7446092869675938,0.27966561432340287,0.6,1.0285584537514965,2.989557883560226,3.044965814893618,0.6749671427573682,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9683333333333333,1.6721574449961667,0.3275689371006418,0.975,2.1663251795251472,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.7883333333333333,0.8872566902596436,0.27966561432340287,0.745,1.153279573853566,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.9366666666666668,2.97826761008284,0.6630614190615611,0.955,4.076327390258402,5.382034194123735,5.231751941184026,0.8576041329426337,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9075,1.832594873095919,0.44252537334483505,0.895,2.4660898168275436,4.515059107298566,4.385233448105733,0.6919478275690014,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9466666666666668,2.83754372010801,0.608791167628598,0.97,3.8789640516448474,4.642710293777947,4.646394978665751,0.8553041170789771,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.9225,1.7364545156585356,0.3998347112567276,0.91,2.3527693088573516,4.228587388499047,4.190294595950896,0.6911269926300146,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.925,2.81162753941394,0.6445514974670188,0.93,3.8526040538084434,4.612699034697736,4.644364135602212,0.8550565538841952,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8891666666666667,1.7245938017850082,0.4367977362673907,0.845,2.342300467013995,4.181116753485783,4.208383450296873,0.6911607404321352,200 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index 5ee880ab..6cdc2b86 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,9 +1,7 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9116666666666667,1.0889543359860974,0.2591017535241754,0.91,1.3866236446708151,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.89,0.6229752808903669,0.15053905583073077,0.905,0.7933914451788466,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9566666666666667,1.2975691237020013,0.2591017535241754,0.97,1.5696656784049068,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9533333333333333,0.7423208325635811,0.15053905583073077,0.96,0.8996786017154156,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9133333333333333,1.4454910752551433,0.3253154503600127,0.96,1.8286646948756324,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.74,0.7892476267585741,0.28546431334778133,0.645,1.0046266703621765,2.989557883560226,3.044965814893618,0.6749671427573682,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9616666666666667,1.7224088521025238,0.3253154503600127,0.99,2.07498069683797,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.825,0.9404465528020834,0.28546431334778133,0.77,1.139020606132637,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.95,3.1884243028188433,0.6875647807747396,0.965,4.048939927801238,5.382034194123735,5.231751941184026,0.8576041329426337,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9066666666666667,1.9778194633199184,0.4796319143787132,0.9,2.49805265701074,4.515059107298566,4.385233448105733,0.6919478275690014,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9366666666666668,3.0424311866970903,0.6450309879669672,0.95,3.8527728896927793,4.642710293777947,4.646394978665751,0.8553041170789771,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.905,1.8776034902629817,0.43604823577765456,0.9,2.3803383019346724,4.228587388499047,4.190294595950896,0.6911269926300146,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.94,2.959079715846458,0.6564246498507638,0.94,3.750120844819083,4.612699034697736,4.644364135602212,0.8550565538841952,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.895,1.8657876384470495,0.46631022079997453,0.86,2.364566875339995,4.181116753485783,4.208383450296873,0.6911607404321352,200 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index 89449231..d59c02e3 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-01 21:52,119.05648648738861,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-02 19:36,79.50826093753179,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index 2f055ded..61abe47e 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,9 +1,7 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9433333333333332,1.0881198449012723,0.25682450851563937,0.945,1.3552240711392805,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.8966666666666667,0.5957046590186345,0.14226747638646786,0.915,0.7443420810052485,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9716666666666667,1.2965747662437588,0.25682450851563937,0.96,1.5447661653225075,3.597108107523503,2.9403917178509804,0.8534357674606872,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9533333333333333,0.7098258823571802,0.14226747638646786,0.955,0.8483239350473682,3.29374008288805,2.748707897836178,0.679591632802887,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9183333333333333,1.4966364514582033,0.3513992824628867,0.94,1.8382232879162173,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.6516666666666667,0.787550236381641,0.30937396820647195,0.6,0.9660154081700578,2.989557883560226,3.044965814893618,0.6749671427573682,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9716666666666667,1.7833523267626594,0.3513992824628867,0.965,2.102235052471558,2.979241703960451,2.9994404795169123,0.8446712058892714,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.775,0.9384239874187676,0.30937396820647195,0.705,1.1067012746087066,2.989557883560226,3.044965814893618,0.6749671427573682,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.94,2.8577555367641163,0.6133825342587731,0.96,3.492613481221878,5.382034194123735,5.231751941184026,0.8576041329426337,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9016666666666667,1.7948773086487813,0.4365123984827818,0.915,2.1646873101396342,4.515059107298566,4.385233448105733,0.6919478275690014,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9466666666666668,2.759668649688763,0.5928088522386004,0.95,3.3907351416744462,4.642710293777947,4.646394978665751,0.8553041170789771,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.9133333333333333,1.705904234575853,0.39417420205664483,0.92,2.07199694125542,4.228587388499047,4.190294595950896,0.6911269926300146,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9066666666666667,2.714971017166016,0.6476594323973127,0.915,3.332849955839899,4.612699034697736,4.644364135602212,0.8550565538841952,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8666666666666667,1.6906675567822227,0.43751542835810414,0.835,2.0555730584579877,4.181116753485783,4.208383450296873,0.6911607404321352,200 diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml index 9418178d..d5e3afbc 100644 --- a/scripts/did/did_pa_multi_tune_config.yml +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -7,8 +7,9 @@ simulation_parameters: n_jobs: -2 dgp_parameters: - DGP: [1, 4] # Different DGP specifications - n_obs: [2000] # Sample size for each simulation (has to be a list) + DGP: [2, 3, 4] # Different DGP specifications + n_obs: [500] # Sample size for each simulation (has to be a list) + xi: [0.2] # Define reusable learner configurations learner_definitions: @@ -34,4 +35,4 @@ dml_parameters: in_sample_normalization: [true] confidence_parameters: - level: [0.95, 0.90] # Confidence levels + level: [0.90] # Confidence levels From 4d4e84e805e33393f7500829b72b8750efc98e49 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Tue, 2 Dec 2025 16:54:18 +0100 Subject: [PATCH 24/51] update dml version in toml --- monte-cover/pyproject.toml | 2 +- monte-cover/uv.lock | 1030 ++++++++++++++++++------------------ 2 files changed, 521 insertions(+), 511 deletions(-) diff --git a/monte-cover/pyproject.toml b/monte-cover/pyproject.toml index 547bf243..f60a3840 100644 --- a/monte-cover/pyproject.toml +++ b/monte-cover/pyproject.toml @@ -9,7 +9,7 @@ authors = [ requires-python = ">=3.12" dependencies = [ "black>=25.1.0", - "doubleml[rdd]>=0.10.0", + "doubleml[rdd]>=0.11.0", "ipykernel>=6.29.5", "itables>=2.2.5", "joblib>=1.4.2", diff --git a/monte-cover/uv.lock b/monte-cover/uv.lock index dd4c0668..2391cbf3 100644 --- a/monte-cover/uv.lock 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results/did/did_pa_multi_group.csv | 96 +++++++++---------- results/did/did_pa_multi_metadata.csv | 2 +- results/did/did_pa_multi_time.csv | 96 +++++++++---------- scripts/did/did_pa_multi_config.yml | 3 +- 8 files changed, 199 insertions(+), 196 deletions(-) diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py index 4a9856b4..df162c65 100644 --- a/monte-cover/src/montecover/did/did_pa_multi.py +++ b/monte-cover/src/montecover/did/did_pa_multi.py @@ -180,7 +180,7 @@ def summarize_results(self): def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData: """Generate data for the simulation.""" - data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"]) + data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"], xi=dgp_params["xi"]) dml_data = dml.data.DoubleMLPanelData( data, y_col="y", diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml index 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+0.12.dev0,DIDMultiCoverageSimulation,2025-12-03 12:45,1189.5563602050145,3.12.3,scripts/did/did_pa_multi_config.yml diff --git a/results/did/did_pa_multi_time.csv b/results/did/did_pa_multi_time.csv index ebe36f51..e11b666e 100644 --- a/results/did/did_pa_multi_time.csv +++ b/results/did/did_pa_multi_time.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.07466666666666666,0.6732674013122543,0.6235690621466867,0.058,0.7961923108719068,500 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.14066666666666666,0.8022475902506702,0.6235690621466867,0.098,0.918006645210409,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.082,0.6058318700175549,0.6008490525254478,0.052,0.7251168811624854,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.136,0.7218931985587499,0.6008490525254478,0.106,0.8351087438145972,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.9,0.9066666666666666,0.59714055238215,0.14040889807066953,0.924,0.7161350746238019,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.9606666666666667,0.7115368548303642,0.14040889807066953,0.962,0.8251205579380226,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.9,0.074,0.6729928370630124,0.6244323144006763,0.056,0.7941715732936923,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.95,0.132,0.8019204267686809,0.6244323144006763,0.104,0.9193016367752481,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.9,0.08666666666666666,0.6059313052481657,0.5993304117564142,0.052,0.725564519463603,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.95,0.12533333333333332,0.7220116829439853,0.5993304117564142,0.098,0.8365327370139422,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.9,0.906,0.5970989989313065,0.14157525005184923,0.92,0.7168446464166246,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.95,0.9566666666666667,0.7114873408397255,0.14157525005184923,0.962,0.8249531167974216,500 -LGBM Regr.,LGBM Clas.,observational,False,1,0.9,0.9486666666666667,0.8770187116658913,0.17962057221172684,0.948,1.061881825210057,500 -LGBM Regr.,LGBM Clas.,observational,False,1,0.95,0.9806666666666666,1.0450322511788936,0.17962057221172684,0.982,1.220684057699607,500 -LGBM Regr.,LGBM Clas.,observational,False,4,0.9,0.9493333333333334,1.3320128962103468,0.2571035149618884,0.94,1.571937612240713,500 -LGBM Regr.,LGBM Clas.,observational,False,4,0.95,0.976,1.5871912617256803,0.2571035149618884,0.972,1.8187323168567295,500 -LGBM Regr.,LGBM Clas.,observational,False,6,0.9,0.946,0.7662860532295224,0.1564515730110081,0.956,0.928463556722575,500 -LGBM Regr.,LGBM Clas.,observational,False,6,0.95,0.9833333333333334,0.9130861503882113,0.1564515730110081,0.988,1.065756941975338,500 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,0.94,0.7816557146222524,0.16791548653175506,0.942,0.9469921947874506,500 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,0.9753333333333334,0.931400231526335,0.16791548653175506,0.978,1.088731036421437,500 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,0.89,1.155715034340344,0.25318614533380285,0.874,1.3683212943129301,500 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,0.944,1.3771194023487263,0.25318614533380285,0.934,1.57988569849374,500 -LGBM Regr.,LGBM Clas.,observational,True,6,0.9,0.9166666666666666,0.7001452111787113,0.15746253827962622,0.916,0.8484234185348815,500 -LGBM Regr.,LGBM Clas.,observational,True,6,0.95,0.9593333333333334,0.8342744760831842,0.15746253827962622,0.96,0.9736662475722498,500 -Linear,Logistic,experimental,False,1,0.9,0.794,0.24425077841473194,0.07578113842525139,0.722,0.3129163378933677,500 -Linear,Logistic,experimental,False,1,0.95,0.8626666666666666,0.2910427536193599,0.07578113842525139,0.848,0.35387918164260174,500 -Linear,Logistic,experimental,False,4,0.9,0.042666666666666665,0.9663030601095306,1.0538679416817638,0.022,1.107527437294173,500 -Linear,Logistic,experimental,False,4,0.95,0.07933333333333333,1.1514211142760844,1.0538679416817638,0.06,1.2869318623991737,500 -Linear,Logistic,experimental,False,6,0.9,0.8973333333333333,0.9667007601125351,0.23778551526074673,0.892,1.1127729718159607,500 -Linear,Logistic,experimental,False,6,0.95,0.944,1.1518950030585073,0.23778551526074673,0.952,1.291361911437721,500 -Linear,Logistic,experimental,True,1,0.9,0.7953333333333333,0.2442344894591128,0.0758026304003878,0.726,0.31326629074244566,500 -Linear,Logistic,experimental,True,1,0.95,0.8626666666666666,0.29102334413158776,0.0758026304003878,0.834,0.3540027436581034,500 -Linear,Logistic,experimental,True,4,0.9,0.041333333333333326,0.9661639682761842,1.0541037636302513,0.028,1.1076075222020547,500 -Linear,Logistic,experimental,True,4,0.95,0.08266666666666665,1.1512553761341393,1.0541037636302513,0.066,1.2890352653966053,500 -Linear,Logistic,experimental,True,6,0.9,0.9,0.9665096797252255,0.23746581622514001,0.902,1.1101278673952202,500 -Linear,Logistic,experimental,True,6,0.95,0.9473333333333334,1.151667316733632,0.23746581622514001,0.95,1.2910188703398415,500 -Linear,Logistic,observational,False,1,0.9,0.8853333333333334,0.2743923693288106,0.06934934364504367,0.898,0.3512416156142327,500 -Linear,Logistic,observational,False,1,0.95,0.9486666666666667,0.3269586744407309,0.06934934364504367,0.956,0.3970891233173115,500 -Linear,Logistic,observational,False,4,0.9,0.19933333333333333,1.3667424492759064,1.0158806375552287,0.166,1.5384295027203043,500 -Linear,Logistic,observational,False,4,0.95,0.284,1.6285740766414527,1.0158806375552287,0.272,1.794614364828469,500 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+Linear,Logistic,observational,False,6,0.9,0.8952380952380953,2.4710693977877525,0.6230644560531012,0.8909090909090909,2.8547374129926615,385 +Linear,Logistic,observational,False,6,0.95,0.941125541125541,2.944460797973462,0.6230644560531012,0.9376623376623376,3.3171432111552077,385 +Linear,Logistic,observational,True,1,0.9,0.8813852813852814,0.5314725438876459,0.13196337160197263,0.8909090909090909,0.6805978579615398,385 +Linear,Logistic,observational,True,1,0.95,0.948051948051948,0.633288596458438,0.13196337160197263,0.9428571428571428,0.7692316890562696,385 +Linear,Logistic,observational,True,4,0.9,0.8112554112554112,2.5516141418016915,0.721408876893265,0.7974025974025974,2.9243151542920063,385 +Linear,Logistic,observational,True,4,0.95,0.883982683982684,3.0404357800780413,0.721408876893265,0.8753246753246753,3.397177212361236,385 +Linear,Logistic,observational,True,6,0.9,0.8761904761904762,2.3249147324810018,0.5931682121755871,0.8649350649350649,2.6878568494364083,385 +Linear,Logistic,observational,True,6,0.95,0.9298701298701298,2.770306772666875,0.5931682121755871,0.922077922077922,3.1197312959254186,385 diff --git a/scripts/did/did_pa_multi_config.yml b/scripts/did/did_pa_multi_config.yml index 2031a60b..60ea86a7 100644 --- a/scripts/did/did_pa_multi_config.yml +++ b/scripts/did/did_pa_multi_config.yml @@ -8,7 +8,8 @@ simulation_parameters: dgp_parameters: DGP: [1, 4, 6] # Different DGP specifications - n_obs: [2000] # Sample size for each simulation (has to be a list) + n_obs: [500] # Sample size for each simulation (has to be a list) + xi: [0.5] # Define reusable learner configurations learner_definitions: From e64099dd42d4ce0306f977982a13f8e78bd44e15 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 3 Dec 2025 19:49:46 +0100 Subject: [PATCH 26/51] run did sim with updated treatment assignment --- doc/did/did_cs_multi.qmd | 10 +- doc/did/did_pa_multi.qmd | 16 ++- .../src/montecover/did/did_cs_multi.py | 11 +++ .../src/montecover/did/did_pa_multi.py | 9 +- results/did/did_cs_multi_config.yml | 24 +---- results/did/did_cs_multi_detailed.csv | 98 +++++++++---------- results/did/did_cs_multi_eventstudy.csv | 98 +++++++++---------- results/did/did_cs_multi_group.csv | 98 +++++++++---------- results/did/did_cs_multi_metadata.csv | 2 +- results/did/did_cs_multi_time.csv | 98 +++++++++---------- results/did/did_pa_multi_config.yml | 26 +---- results/did/did_pa_multi_detailed.csv | 98 +++++++++---------- results/did/did_pa_multi_eventstudy.csv | 98 +++++++++---------- results/did/did_pa_multi_group.csv | 98 +++++++++---------- results/did/did_pa_multi_metadata.csv | 2 +- results/did/did_pa_multi_time.csv | 98 +++++++++---------- results/did/did_pa_multi_tune_config.yml | 10 +- results/did/did_pa_multi_tune_detailed.csv | 14 +-- results/did/did_pa_multi_tune_eventstudy.csv | 14 +-- results/did/did_pa_multi_tune_group.csv | 14 +-- results/did/did_pa_multi_tune_metadata.csv | 2 +- results/did/did_pa_multi_tune_time.csv | 14 +-- scripts/did/did_cs_multi_config.yml | 24 +---- scripts/did/did_pa_multi_config.yml | 25 +---- scripts/did/did_pa_multi_tune_config.yml | 9 +- 25 files changed, 469 insertions(+), 541 deletions(-) diff --git a/doc/did/did_cs_multi.qmd b/doc/did/did_cs_multi.qmd index 81b9441b..e6c938c7 100644 --- a/doc/did/did_cs_multi.qmd +++ b/doc/did/did_cs_multi.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## Coverage -The simulations are based on the [make_did_cs_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_cs_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: +The simulations are based on the [make_did_cs_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_cs_CS2021.html)-DGP with $1000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: - Type 1: Linear outcome model and treatment assignment - Type 4: Nonlinear outcome model and treatment assignment @@ -52,7 +52,7 @@ df = pd.read_csv("../../results/did/did_cs_multi_detailed.csv", index_col=None) assert df["repetition"].nunique() == 1 n_rep = df["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_d0_t0", "Loss g_d0_t1", "Loss g_d1_t0", "Loss g_d1_t1", "Loss m"] ``` ### Observational Score @@ -127,7 +127,7 @@ df_group = pd.read_csv("../../results/did/did_cs_multi_group.csv", index_col=Non assert df_group["repetition"].nunique() == 1 n_rep_group = df_group["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_d0_t0", "Loss g_d0_t1", "Loss g_d1_t0", "Loss g_d1_t1", "Loss m"] ``` #### Observational Score @@ -195,7 +195,7 @@ df_time = pd.read_csv("../../results/did/did_cs_multi_time.csv", index_col=None) assert df_time["repetition"].nunique() == 1 n_rep_time = df_time["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_d0_t0", "Loss g_d0_t1", "Loss g_d1_t0", "Loss g_d1_t1", "Loss m"] ``` #### Observational Score @@ -263,7 +263,7 @@ df_es = pd.read_csv("../../results/did/did_cs_multi_eventstudy.csv", index_col=N assert df_es["repetition"].nunique() == 1 n_rep_es = df_es["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_d0_t0", "Loss g_d0_t1", "Loss g_d1_t0", "Loss g_d1_t1", "Loss m"] ``` #### Observational Score diff --git a/doc/did/did_pa_multi.qmd b/doc/did/did_pa_multi.qmd index 823cc5b3..33934b24 100644 --- a/doc/did/did_pa_multi.qmd +++ b/doc/did/did_pa_multi.qmd @@ -24,7 +24,7 @@ init_notebook_mode(all_interactive=True) ## Coverage -The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: +The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $1000$ observations. Learners are both set to either boosting or a linear (logistic) model. Due to time constraints we only consider the following DGPs: - Type 1: Linear outcome model and treatment assignment - Type 4: Nonlinear outcome model and treatment assignment @@ -52,7 +52,7 @@ df = pd.read_csv("../../results/did/did_pa_multi_detailed.csv", index_col=None) assert df["repetition"].nunique() == 1 n_rep = df["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` ### Observational Score @@ -127,7 +127,7 @@ df_group = pd.read_csv("../../results/did/did_pa_multi_group.csv", index_col=Non assert df_group["repetition"].nunique() == 1 n_rep_group = df_group["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -195,7 +195,7 @@ df_time = pd.read_csv("../../results/did/did_pa_multi_time.csv", index_col=None) assert df_time["repetition"].nunique() == 1 n_rep_time = df_time["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -263,7 +263,7 @@ df_es = pd.read_csv("../../results/did/did_pa_multi_eventstudy.csv", index_col=N assert df_es["repetition"].nunique() == 1 n_rep_es = df_es["repetition"].unique()[0] -display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage"] +display_columns = ["Learner g", "Learner m", "DGP", "In-sample-norm.", "Bias", "CI Length", "Coverage", "Uniform CI Length", "Uniform Coverage", "Loss g_control", "Loss g_treated", "Loss m"] ``` #### Observational Score @@ -324,11 +324,9 @@ generate_and_show_styled_table( ## Tuning -The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $2000$ observations. Due to time constraints we only consider one learner, use in-sample normalization and the following DGPs: +The simulations are based on the the [make_did_CS2021](https://docs.doubleml.org/stable/api/generated/doubleml.did.datasets.make_did_CS2021.html)-DGP with $1000$ observations. Due to time constraints we only consider one learner, use in-sample normalization and the following DGPs: - Type 1: Linear outcome model and treatment assignment - - Type 2: Nonlinear outcome model and linear treatment assignment - - Type 3: Linear outcome model and nonlinear treatment assignment - Type 4: Nonlinear outcome model and treatment assignment The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. @@ -389,8 +387,6 @@ These simulations test different types of aggregation, as described in [DiD User As before, we only consider one learner, use in-sample normalization and the following DGPs: - Type 1: Linear outcome model and treatment assignment - - Type 2: Nonlinear outcome model and linear treatment assignment - - Type 3: Linear outcome model and nonlinear treatment assignment - Type 4: Nonlinear outcome model and treatment assignment The non-uniform results (coverage, ci length and bias) refer to averaged values over all $ATTs$ (point-wise confidende intervals). This is only an example as the untuned version just relies on the default configuration. diff --git a/monte-cover/src/montecover/did/did_cs_multi.py b/monte-cover/src/montecover/did/did_cs_multi.py index a901cdc7..9882fdd8 100644 --- a/monte-cover/src/montecover/did/did_cs_multi.py +++ b/monte-cover/src/montecover/did/did_cs_multi.py @@ -96,6 +96,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ) dml_model.fit() dml_model.bootstrap(n_rep_boot=2000) + nuisance_loss = dml_model.nuisance_loss # Oracle values for this model oracle_thetas = np.full_like(dml_model.coef, np.nan) @@ -143,6 +144,11 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Score": score, "In-sample-norm.": in_sample_normalization, "level": level, + "Loss g_d0_t0": nuisance_loss["ml_g_d0_t0"].mean(), + "Loss g_d1_t0": nuisance_loss["ml_g_d1_t0"].mean(), + "Loss g_d0_t1": nuisance_loss["ml_g_d0_t1"].mean(), + "Loss g_d1_t1": nuisance_loss["ml_g_d1_t1"].mean(), + "Loss m": nuisance_loss["ml_m"].mean() if score == "observational" else np.nan, } ) for key, res in level_result.items(): @@ -168,6 +174,11 @@ def summarize_results(self): "Bias": "mean", "Uniform Coverage": "mean", "Uniform CI Length": "mean", + "Loss g_d0_t0": "mean", + "Loss g_d1_t0": "mean", + "Loss g_d0_t1": "mean", + "Loss g_d1_t1": "mean", + "Loss m": "mean", "repetition": "count", } diff --git a/monte-cover/src/montecover/did/did_pa_multi.py b/monte-cover/src/montecover/did/did_pa_multi.py index df162c65..5ac90b99 100644 --- a/monte-cover/src/montecover/did/did_pa_multi.py +++ b/monte-cover/src/montecover/did/did_pa_multi.py @@ -94,6 +94,7 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: ) dml_model.fit() dml_model.bootstrap(n_rep_boot=2000) + nuisance_loss = dml_model.nuisance_loss # Oracle values for this model oracle_thetas = np.full_like(dml_model.coef, np.nan) @@ -141,6 +142,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]: "Score": score, "In-sample-norm.": in_sample_normalization, "level": level, + "Loss g_control": nuisance_loss["ml_g0"].mean(), + "Loss g_treated": nuisance_loss["ml_g1"].mean(), + "Loss m": nuisance_loss["ml_m"].mean() if score == "observational" else np.nan, } ) for key, res in level_result.items(): @@ -166,6 +170,9 @@ def summarize_results(self): "Bias": "mean", "Uniform Coverage": "mean", "Uniform CI Length": "mean", + "Loss g_control": "mean", + "Loss g_treated": "mean", + "Loss m": "mean", "repetition": "count", } @@ -180,7 +187,7 @@ def summarize_results(self): def _generate_dml_data(self, dgp_params) -> dml.data.DoubleMLPanelData: """Generate data for the simulation.""" - data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"], xi=dgp_params["xi"]) + data = make_did_CS2021(n_obs=dgp_params["n_obs"], dgp_type=dgp_params["DGP"]) dml_data = dml.data.DoubleMLPanelData( data, y_col="y", diff --git a/results/did/did_cs_multi_config.yml b/results/did/did_cs_multi_config.yml index d469e0b4..99d4329c 100644 --- a/results/did/did_cs_multi_config.yml +++ b/results/did/did_cs_multi_config.yml @@ -9,7 +9,7 @@ dgp_parameters: - 4 - 6 n_obs: - - 2000 + - 1000 lambda_t: - 0.5 learner_definitions: @@ -19,30 +19,8 @@ learner_definitions: name: Logistic lgbmr: &id003 name: LGBM Regr. - params: - n_estimators: 300 - learning_rate: 0.03 - num_leaves: 7 - max_depth: 3 - min_child_samples: 20 - subsample: 0.8 - colsample_bytree: 0.8 - reg_alpha: 0.1 - reg_lambda: 1.0 - random_state: 42 lgbmc: &id004 name: LGBM Clas. - params: - n_estimators: 300 - learning_rate: 0.03 - num_leaves: 7 - max_depth: 3 - min_child_samples: 20 - subsample: 0.8 - colsample_bytree: 0.8 - reg_alpha: 0.1 - reg_lambda: 1.0 - random_state: 42 dml_parameters: learners: - ml_g: *id001 diff --git a/results/did/did_cs_multi_detailed.csv b/results/did/did_cs_multi_detailed.csv index d1d95ab7..e36dbf2b 100644 --- a/results/did/did_cs_multi_detailed.csv +++ b/results/did/did_cs_multi_detailed.csv @@ -1,49 +1,49 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a/results/did/did_cs_multi_time.csv b/results/did/did_cs_multi_time.csv index 1f15f2c4..fc3aa48c 100644 --- a/results/did/did_cs_multi_time.csv +++ b/results/did/did_cs_multi_time.csv @@ -1,49 +1,49 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.7060931899641577,1.6951392436550388,0.6480352394763556,0.6164874551971327,2.1760652275678454,279 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.8100358422939068,2.019882992568775,0.6480352394763556,0.7813620071684588,2.45754914651766,279 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.6774193548387096,1.6567161125525725,0.6736701567456025,0.5770609318996416,2.1275235767067735,279 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.7873357228195937,1.9740990079636107,0.6736701567456025,0.7204301075268817,2.402986793943087,279 -LGBM Regr.,LGBM 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+Linear,Logistic,observational,True,4,0.95,0.9533333333333334,5.740399656142881,1.15017802387419,0.952,6.984976906123499,7.323380222410015,7.344796263558695,11.642358468389633,11.684277895003406,0.6726867966907344,500 +Linear,Logistic,observational,True,6,0.9,0.9693333333333334,4.240954640807382,0.79925330830394,0.976,5.449619864334082,7.545350590491914,7.557135124700455,12.030935164798441,12.038482179518887,0.6939598068809577,500 +Linear,Logistic,observational,True,6,0.95,0.9866666666666666,5.053409142220106,0.79925330830394,0.996,6.142269320274921,7.545350590491914,7.557135124700455,12.030935164798441,12.038482179518887,0.6939598068809577,500 diff --git a/results/did/did_pa_multi_config.yml b/results/did/did_pa_multi_config.yml index 537a21d9..0964a5db 100644 --- a/results/did/did_pa_multi_config.yml +++ b/results/did/did_pa_multi_config.yml @@ -9,9 +9,7 @@ dgp_parameters: - 4 - 6 n_obs: - - 500 - xi: - - 0.5 + - 1000 learner_definitions: linear: &id001 name: Linear @@ -19,30 +17,8 @@ learner_definitions: name: Logistic lgbmr: &id003 name: LGBM Regr. - params: - n_estimators: 300 - learning_rate: 0.03 - num_leaves: 7 - max_depth: 3 - min_child_samples: 20 - subsample: 0.8 - colsample_bytree: 0.8 - reg_alpha: 0.1 - reg_lambda: 1.0 - random_state: 42 lgbmc: &id004 name: LGBM Clas. - params: - n_estimators: 300 - learning_rate: 0.03 - num_leaves: 7 - max_depth: 3 - min_child_samples: 20 - subsample: 0.8 - colsample_bytree: 0.8 - reg_alpha: 0.1 - reg_lambda: 1.0 - random_state: 42 dml_parameters: learners: - ml_g: *id001 diff --git a/results/did/did_pa_multi_detailed.csv b/results/did/did_pa_multi_detailed.csv index 657fec60..23642ea3 100644 --- a/results/did/did_pa_multi_detailed.csv +++ b/results/did/did_pa_multi_detailed.csv @@ -1,49 +1,49 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.8043290043290043,2.1154639149911363,0.6712389430932117,0.7584415584415585,3.0637822426820147,385 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.8807359307359307,2.520730730102257,0.6712389430932117,0.8363636363636363,3.4177267443343715,385 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.8034632034632034,1.8291244336921813,0.6041960287714497,0.7116883116883117,2.71039040553167,385 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.867965367965368,2.179536193699663,0.6041960287714497,0.787012987012987,3.007554855387085,385 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.9,0.8893939393939394,1.8257020486735736,0.44752934085767393,0.8883116883116883,2.707519362778072,385 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.9448051948051948,2.175458170422824,0.44752934085767393,0.9454545454545454,3.0075896607904156,385 -LGBM Regr.,LGBM 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170cd1d6..c14ef0d6 100644 --- a/results/did/did_pa_multi_metadata.csv +++ b/results/did/did_pa_multi_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiCoverageSimulation,2025-12-03 12:45,1189.5563602050145,3.12.3,scripts/did/did_pa_multi_config.yml +0.12.dev0,DIDMultiCoverageSimulation,2025-12-03 18:27,12.992252508799234,3.12.9,scripts/did/did_pa_multi_config.yml diff --git a/results/did/did_pa_multi_time.csv b/results/did/did_pa_multi_time.csv index e11b666e..9949ef7d 100644 --- a/results/did/did_pa_multi_time.csv +++ b/results/did/did_pa_multi_time.csv @@ -1,49 +1,49 @@ -Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.7541125541125541,2.1082420518414065,0.7393028675659481,0.7428571428571429,2.438577611128698,385 -LGBM Regr.,LGBM 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-Linear,Logistic,observational,False,6,0.9,0.8952380952380953,2.4710693977877525,0.6230644560531012,0.8909090909090909,2.8547374129926615,385 -Linear,Logistic,observational,False,6,0.95,0.941125541125541,2.944460797973462,0.6230644560531012,0.9376623376623376,3.3171432111552077,385 -Linear,Logistic,observational,True,1,0.9,0.8813852813852814,0.5314725438876459,0.13196337160197263,0.8909090909090909,0.6805978579615398,385 -Linear,Logistic,observational,True,1,0.95,0.948051948051948,0.633288596458438,0.13196337160197263,0.9428571428571428,0.7692316890562696,385 -Linear,Logistic,observational,True,4,0.9,0.8112554112554112,2.5516141418016915,0.721408876893265,0.7974025974025974,2.9243151542920063,385 -Linear,Logistic,observational,True,4,0.95,0.883982683982684,3.0404357800780413,0.721408876893265,0.8753246753246753,3.397177212361236,385 -Linear,Logistic,observational,True,6,0.9,0.8761904761904762,2.3249147324810018,0.5931682121755871,0.8649350649350649,2.6878568494364083,385 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Clas.,observational,True,4,0.9,0.9253333333333333,2.1769818470918034,0.4876866398709783,0.922,2.6607655660074245,3.6214703404808577,3.6516561199001734,0.8538647372576891,500 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,0.966,2.594033867442298,0.4876866398709783,0.968,3.0491731931810295,3.6214703404808577,3.6516561199001734,0.8538647372576891,500 +LGBM Regr.,LGBM Clas.,observational,True,6,0.9,0.926,1.6134598462522334,0.3660004462666269,0.93,1.9965324681411751,3.6623282629507616,3.667380623747617,0.8821558756328344,500 +LGBM Regr.,LGBM Clas.,observational,True,6,0.95,0.9693333333333334,1.9225559875603495,0.3660004462666269,0.982,2.281265377129387,3.6623282629507616,3.667380623747617,0.8821558756328344,500 +Linear,Logistic,experimental,False,1,0.9,0.8526666666666667,0.3477880977154597,0.0941528517928185,0.836,0.44587367405570344,1.4305249692200885,1.432190456683633,,500 +Linear,Logistic,experimental,False,1,0.95,0.9146666666666666,0.41441507901061925,0.0941528517928185,0.896,0.5036628412337449,1.4305249692200885,1.432190456683633,,500 +Linear,Logistic,experimental,False,4,0.9,0.3253333333333333,1.3752446336807453,0.9094526360701841,0.296,1.5778638502429552,4.624958585767405,4.6817355343203015,,500 +Linear,Logistic,experimental,False,4,0.95,0.428,1.6387050542253287,0.9094526360701841,0.406,1.83676396020269,4.624958585767405,4.6817355343203015,,500 +Linear,Logistic,experimental,False,6,0.9,0.898,1.3716587518602372,0.33862377559514284,0.888,1.5798273997533752,4.807293046724782,4.8155355801041395,,500 +Linear,Logistic,experimental,False,6,0.95,0.942,1.634432212492877,0.33862377559514284,0.942,1.8350694583673781,4.807293046724782,4.8155355801041395,,500 +Linear,Logistic,experimental,True,1,0.9,0.8506666666666667,0.34778576330199173,0.0940232271229107,0.844,0.4456730260708535,1.4304053019738947,1.4322187590435955,,500 +Linear,Logistic,experimental,True,1,0.95,0.9166666666666666,0.41441229738540525,0.0940232271229107,0.894,0.5034320949442403,1.4304053019738947,1.4322187590435955,,500 +Linear,Logistic,experimental,True,4,0.9,0.3213333333333333,1.3750393526707052,0.9082732868315869,0.3,1.578432029421048,4.624387165972086,4.681683236630598,,500 +Linear,Logistic,experimental,True,4,0.95,0.4266666666666667,1.638460446814799,0.9082732868315869,0.406,1.833871345581639,4.624387165972086,4.681683236630598,,500 +Linear,Logistic,experimental,True,6,0.9,0.8973333333333333,1.3716503622931648,0.33860137170621196,0.884,1.5788212139938895,4.80864345960688,4.816156156306712,,500 +Linear,Logistic,experimental,True,6,0.95,0.9373333333333334,1.6344222157071213,0.33860137170621196,0.94,1.8305678280809632,4.80864345960688,4.816156156306712,,500 +Linear,Logistic,observational,False,1,0.9,0.8873333333333334,0.3842361494786204,0.09502707859290638,0.886,0.4927986549739525,1.430409587506778,1.4324863844056221,0.6762284233665801,500 +Linear,Logistic,observational,False,1,0.95,0.944,0.4578456114251332,0.09502707859290638,0.94,0.5558001935505136,1.430409587506778,1.4324863844056221,0.6762284233665801,500 +Linear,Logistic,observational,False,4,0.9,0.5346666666666666,1.8588178500844048,0.8564365743323401,0.508,2.1091189235370176,4.624523121930485,4.681651215529987,0.6756504408066538,500 +Linear,Logistic,observational,False,4,0.95,0.6506666666666666,2.214918081639791,0.8564365743323401,0.63,2.457644312114233,4.624523121930485,4.681651215529987,0.6756504408066538,500 +Linear,Logistic,observational,False,6,0.9,0.9086666666666666,1.5774820636844875,0.3911546821727288,0.906,1.8125445410478398,4.807036455087926,4.8147015452372015,0.6974024454123922,500 +Linear,Logistic,observational,False,6,0.95,0.948,1.879685815454467,0.3911546821727288,0.948,2.104376893041679,4.807036455087926,4.8147015452372015,0.6974024454123922,500 +Linear,Logistic,observational,True,1,0.9,0.8946666666666666,0.37926856313083895,0.09420520289392965,0.864,0.48615414980107297,1.4305198797465812,1.4323544622750601,0.676227032403548,500 +Linear,Logistic,observational,True,1,0.95,0.9366666666666666,0.45192636720047263,0.09420520289392965,0.936,0.5486757817571685,1.4305198797465812,1.4323544622750601,0.676227032403548,500 +Linear,Logistic,observational,True,4,0.9,0.5346666666666666,1.831437355776531,0.8595460490404199,0.492,2.0766874108184137,4.625870510476999,4.681992245779976,0.6756920923702538,500 +Linear,Logistic,observational,True,4,0.95,0.6426666666666666,2.1822922103506857,0.8595460490404199,0.604,2.4229290289492678,4.625870510476999,4.681992245779976,0.6756920923702538,500 +Linear,Logistic,observational,True,6,0.9,0.9033333333333333,1.5341853329208817,0.39061849014153127,0.906,1.7621100729507977,4.807530825853383,4.815937542018182,0.6974023190896237,500 +Linear,Logistic,observational,True,6,0.95,0.9473333333333334,1.8280945786692995,0.39061849014153127,0.94,2.047037540792628,4.807530825853383,4.815937542018182,0.6974023190896237,500 diff --git a/results/did/did_pa_multi_tune_config.yml b/results/did/did_pa_multi_tune_config.yml index be727ea3..cbbfbb24 100644 --- a/results/did/did_pa_multi_tune_config.yml +++ b/results/did/did_pa_multi_tune_config.yml @@ -1,17 +1,14 @@ simulation_parameters: - repetitions: 200 + repetitions: 100 max_runtime: 19800 random_seed: 42 n_jobs: -2 dgp_parameters: DGP: - - 2 - - 3 + - 1 - 4 n_obs: - - 500 - xi: - - 0.2 + - 1000 learner_definitions: lgbmr: &id001 name: LGBM Regr. @@ -29,4 +26,5 @@ dml_parameters: - true confidence_parameters: level: + - 0.95 - 0.9 diff --git a/results/did/did_pa_multi_tune_detailed.csv b/results/did/did_pa_multi_tune_detailed.csv index f100f991..2a5145bc 100644 --- a/results/did/did_pa_multi_tune_detailed.csv +++ b/results/did/did_pa_multi_tune_detailed.csv @@ -1,7 +1,9 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.9266666666666667,3.0908848408903418,0.7161526833648638,0.96,4.742031668154074,5.382034194123735,5.231751941184026,0.8576041329426337,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.90125,1.8794931781846032,0.4591493790044287,0.865,2.8579990686983887,4.515059107298566,4.385233448105733,0.6919478275690014,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9308333333333333,2.947927939580188,0.6723441057011548,0.96,4.521838848419626,4.642710293777947,4.646394978665751,0.8553041170789771,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.905,1.8011025121556412,0.4236148425151305,0.885,2.7558988253099552,4.228587388499047,4.190294595950896,0.6911269926300146,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.925,2.9018541428866937,0.6722860434862635,0.94,4.462288809787092,4.612699034697736,4.644364135602212,0.8550565538841952,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8904166666666667,1.7864285303816843,0.4480420728291524,0.88,2.7393757933996197,4.181116753485783,4.208383450296873,0.6911607404321352,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9275,1.8593949776820482,0.4346861300386444,0.97,2.874851679530885,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.9075,1.088851048708071,0.2557955033769076,0.93,1.6741086802905776,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9758333333333333,2.215605771588204,0.4346861300386444,1.0,3.1578645489164607,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9483333333333333,1.297446049319165,0.2557955033769076,0.97,1.8440233690565457,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9108333333333333,1.8075508119563164,0.44789904815007936,0.96,2.802110311494384,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8933333333333333,1.058831441283919,0.26192371622931565,0.89,1.6345701645609878,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9683333333333333,2.153829637854477,0.44789904815007936,0.98,3.0783331083010848,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.9441666666666667,1.261675480791182,0.26192371622931565,0.96,1.7976149308747436,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 diff --git a/results/did/did_pa_multi_tune_eventstudy.csv b/results/did/did_pa_multi_tune_eventstudy.csv index b603f6f4..5ae81091 100644 --- a/results/did/did_pa_multi_tune_eventstudy.csv +++ b/results/did/did_pa_multi_tune_eventstudy.csv @@ -1,7 +1,9 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.9366666666666668,2.97826761008284,0.6630614190615611,0.955,4.076327390258402,5.382034194123735,5.231751941184026,0.8576041329426337,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9075,1.832594873095919,0.44252537334483505,0.895,2.4660898168275436,4.515059107298566,4.385233448105733,0.6919478275690014,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9466666666666668,2.83754372010801,0.608791167628598,0.97,3.8789640516448474,4.642710293777947,4.646394978665751,0.8553041170789771,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.9225,1.7364545156585356,0.3998347112567276,0.91,2.3527693088573516,4.228587388499047,4.190294595950896,0.6911269926300146,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.925,2.81162753941394,0.6445514974670188,0.93,3.8526040538084434,4.612699034697736,4.644364135602212,0.8550565538841952,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8891666666666667,1.7245938017850082,0.4367977362673907,0.845,2.342300467013995,4.181116753485783,4.208383450296873,0.6911607404321352,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9416666666666668,1.7659541047891871,0.39308694266139194,0.93,2.4420578877328767,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.9083333333333333,1.030714180672431,0.24016490667021678,0.93,1.406859901790291,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9733333333333333,2.1042641041272394,0.39308694266139194,1.0,2.736959872483268,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.96,1.2281716983029005,0.24016490667021678,0.96,1.5807763156565435,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9366666666666668,1.7112550769045318,0.396163312403484,0.98,2.367964369489649,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.9,0.9994035644332807,0.25096323583763025,0.88,1.3714882023841133,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.9766666666666667,2.0390861923139108,0.396163312403484,0.99,2.6584225369437418,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.95,1.1908627978895385,0.25096323583763025,0.91,1.5408540922037406,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 diff --git a/results/did/did_pa_multi_tune_group.csv b/results/did/did_pa_multi_tune_group.csv index 6cdc2b86..4b28278d 100644 --- a/results/did/did_pa_multi_tune_group.csv +++ b/results/did/did_pa_multi_tune_group.csv @@ -1,7 +1,9 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.95,3.1884243028188433,0.6875647807747396,0.965,4.048939927801238,5.382034194123735,5.231751941184026,0.8576041329426337,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9066666666666667,1.9778194633199184,0.4796319143787132,0.9,2.49805265701074,4.515059107298566,4.385233448105733,0.6919478275690014,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9366666666666668,3.0424311866970903,0.6450309879669672,0.95,3.8527728896927793,4.642710293777947,4.646394978665751,0.8553041170789771,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.905,1.8776034902629817,0.43604823577765456,0.9,2.3803383019346724,4.228587388499047,4.190294595950896,0.6911269926300146,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.94,2.959079715846458,0.6564246498507638,0.94,3.750120844819083,4.612699034697736,4.644364135602212,0.8550565538841952,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.895,1.8657876384470495,0.46631022079997453,0.86,2.364566875339995,4.181116753485783,4.208383450296873,0.6911607404321352,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9366666666666668,1.887529422761483,0.4078406757751923,0.97,2.3923419517019577,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.9133333333333333,1.1253315787859015,0.25856668346071443,0.9,1.429389738623314,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9866666666666667,2.2491300306330113,0.4078406757751923,0.99,2.717316302560584,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.96,1.340915282032592,0.25856668346071443,0.96,1.6146455048175112,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9366666666666668,1.8035237527862522,0.4277250604905066,0.97,2.2879701408286475,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.9033333333333333,1.0877850089585135,0.2646623250717837,0.93,1.3792469402376673,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.98,2.149031100885831,0.4277250604905066,0.99,2.598740285490513,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.9533333333333333,1.2961757846092934,0.2646623250717837,0.96,1.5703576274240174,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 diff --git a/results/did/did_pa_multi_tune_metadata.csv b/results/did/did_pa_multi_tune_metadata.csv index d59c02e3..ae879633 100644 --- a/results/did/did_pa_multi_tune_metadata.csv +++ b/results/did/did_pa_multi_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-02 19:36,79.50826093753179,3.12.9,scripts/did/did_pa_multi_tune_config.yml +0.12.dev0,DIDMultiTuningCoverageSimulation,2025-12-03 19:48,38.914858877658844,3.12.9,scripts/did/did_pa_multi_tune_config.yml diff --git a/results/did/did_pa_multi_tune_time.csv b/results/did/did_pa_multi_tune_time.csv index 61abe47e..859e4a64 100644 --- a/results/did/did_pa_multi_tune_time.csv +++ b/results/did/did_pa_multi_tune_time.csv @@ -1,7 +1,9 @@ Learner g,Learner m,Score,Control Group,In-sample-norm.,DGP,level,Tuned,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,False,0.94,2.8577555367641163,0.6133825342587731,0.96,3.492613481221878,5.382034194123735,5.231751941184026,0.8576041329426337,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,2,0.9,True,0.9016666666666667,1.7948773086487813,0.4365123984827818,0.915,2.1646873101396342,4.515059107298566,4.385233448105733,0.6919478275690014,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,False,0.9466666666666668,2.759668649688763,0.5928088522386004,0.95,3.3907351416744462,4.642710293777947,4.646394978665751,0.8553041170789771,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,3,0.9,True,0.9133333333333333,1.705904234575853,0.39417420205664483,0.92,2.07199694125542,4.228587388499047,4.190294595950896,0.6911269926300146,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9066666666666667,2.714971017166016,0.6476594323973127,0.915,3.332849955839899,4.612699034697736,4.644364135602212,0.8550565538841952,200 -LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8666666666666667,1.6906675567822227,0.43751542835810414,0.835,2.0555730584579877,4.181116753485783,4.208383450296873,0.6911607404321352,200 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,False,0.9433333333333332,1.7120476117205499,0.36849188080191575,0.95,2.1085453606546345,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.9,True,0.9233333333333333,1.016949457432688,0.22638709701961932,0.92,1.241298139223119,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,False,0.9766666666666667,2.0400305557943064,0.36849188080191575,0.98,2.406647044968694,4.15228593515413,4.102044299300661,0.8799534697781108,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,1,0.95,True,0.9633333333333333,1.211770018928512,0.22638709701961932,0.98,1.4246578476359533,3.6012775766109666,3.6101319424740215,0.693616349570638,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,False,0.9266666666666667,1.649219987325804,0.3783409546428835,0.95,2.036331118231633,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.9,True,0.8833333333333333,0.9868002437637116,0.251633620630943,0.92,1.212057651508958,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,False,0.98,1.9651668238304254,0.3783409546428835,0.97,2.325492733909296,3.652463306210211,3.6708533097124714,0.8830643738769425,100 +LGBM Regr.,LGBM Clas.,observational,never_treated,True,4,0.95,True,0.9533333333333333,1.1758450150344482,0.251633620630943,0.96,1.388067825552566,3.5143385620781493,3.5272128458553693,0.6931310978755358,100 diff --git a/scripts/did/did_cs_multi_config.yml b/scripts/did/did_cs_multi_config.yml index f1cdc060..471ed7ee 100644 --- a/scripts/did/did_cs_multi_config.yml +++ b/scripts/did/did_cs_multi_config.yml @@ -8,7 +8,7 @@ simulation_parameters: dgp_parameters: DGP: [1, 4, 6] # Different DGP specifications - n_obs: [2000] # Sample size for each simulation (has to be a list) + n_obs: [1000] # Sample size for each simulation (has to be a list) lambda_t: [0.5] # Define reusable learner configurations @@ -21,31 +21,9 @@ learner_definitions: lgbmr: &lgbmr name: "LGBM Regr." - params: - n_estimators: 300 # More trees to learn slowly and steadily - learning_rate: 0.03 # Lower learning rate to improve generalization - num_leaves: 7 # Fewer leaves — simpler trees - max_depth: 3 # Shallow trees reduce overfitting - min_child_samples: 20 # Require more samples per leaf - subsample: 0.8 # More row sampling to add randomness - colsample_bytree: 0.8 # More feature sampling - reg_alpha: 0.1 # Add L1 regularization - reg_lambda: 1.0 # Increase L2 regularization - random_state: 42 # Reproducible lgbmc: &lgbmc name: "LGBM Clas." - params: - n_estimators: 300 # More trees to learn slowly and steadily - learning_rate: 0.03 # Lower learning rate to improve generalization - num_leaves: 7 # Fewer leaves — simpler trees - max_depth: 3 # Shallow trees reduce overfitting - min_child_samples: 20 # Require more samples per leaf - subsample: 0.8 # More row sampling to add randomness - colsample_bytree: 0.8 # More feature sampling - reg_alpha: 0.1 # Add L1 regularization - reg_lambda: 1.0 # Increase L2 regularization - random_state: 42 # Reproducible dml_parameters: # ML methods for ml_g and ml_m diff --git a/scripts/did/did_pa_multi_config.yml b/scripts/did/did_pa_multi_config.yml index 60ea86a7..eb12a183 100644 --- a/scripts/did/did_pa_multi_config.yml +++ b/scripts/did/did_pa_multi_config.yml @@ -8,8 +8,7 @@ simulation_parameters: dgp_parameters: DGP: [1, 4, 6] # Different DGP specifications - n_obs: [500] # Sample size for each simulation (has to be a list) - xi: [0.5] + n_obs: [1000] # Sample size for each simulation (has to be a list) # Define reusable learner configurations learner_definitions: @@ -21,31 +20,9 @@ learner_definitions: lgbmr: &lgbmr name: "LGBM Regr." - params: - n_estimators: 300 # More trees to learn slowly and steadily - learning_rate: 0.03 # Lower learning rate to improve generalization - num_leaves: 7 # Fewer leaves — simpler trees - max_depth: 3 # Shallow trees reduce overfitting - min_child_samples: 20 # Require more samples per leaf - subsample: 0.8 # More row sampling to add randomness - colsample_bytree: 0.8 # More feature sampling - reg_alpha: 0.1 # Add L1 regularization - reg_lambda: 1.0 # Increase L2 regularization - random_state: 42 # Reproducible lgbmc: &lgbmc name: "LGBM Clas." - params: - n_estimators: 300 # More trees to learn slowly and steadily - learning_rate: 0.03 # Lower learning rate to improve generalization - num_leaves: 7 # Fewer leaves — simpler trees - max_depth: 3 # Shallow trees reduce overfitting - min_child_samples: 20 # Require more samples per leaf - subsample: 0.8 # More row sampling to add randomness - colsample_bytree: 0.8 # More feature sampling - reg_alpha: 0.1 # Add L1 regularization - reg_lambda: 1.0 # Increase L2 regularization - random_state: 42 # Reproducible dml_parameters: # ML methods for ml_g and ml_m diff --git a/scripts/did/did_pa_multi_tune_config.yml b/scripts/did/did_pa_multi_tune_config.yml index d5e3afbc..90f1c808 100644 --- a/scripts/did/did_pa_multi_tune_config.yml +++ b/scripts/did/did_pa_multi_tune_config.yml @@ -1,15 +1,14 @@ # Simulation parameters for DID Multi Coverage simulation_parameters: - repetitions: 200 + repetitions: 100 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 dgp_parameters: - DGP: [2, 3, 4] # Different DGP specifications - n_obs: [500] # Sample size for each simulation (has to be a list) - xi: [0.2] + DGP: [1, 4] # Different DGP specifications + n_obs: [1000] # Sample size for each simulation (has to be a list) # Define reusable learner configurations learner_definitions: @@ -35,4 +34,4 @@ dml_parameters: in_sample_normalization: [true] confidence_parameters: - level: [0.90] # Confidence levels + level: [0.95, 0.90] # Confidence levels From 4f425d3aa3cae02f4c4f3d7179e4b108c784d5a8 Mon Sep 17 00:00:00 2001 From: SvenKlaassen Date: Wed, 3 Dec 2025 21:41:07 +0100 Subject: [PATCH 27/51] rerun plr tuning --- results/plm/plr_ate_tune_config.yml | 2 +- results/plm/plr_ate_tune_coverage.csv | 8 ++++---- results/plm/plr_ate_tune_metadata.csv | 2 +- scripts/plm/plr_ate_tune_config.yml | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/results/plm/plr_ate_tune_config.yml b/results/plm/plr_ate_tune_config.yml index 17875c95..9893ef17 100644 --- a/results/plm/plr_ate_tune_config.yml +++ b/results/plm/plr_ate_tune_config.yml @@ -1,5 +1,5 @@ simulation_parameters: - repetitions: 200 + repetitions: 500 max_runtime: 19800 random_seed: 42 n_jobs: -2 diff --git a/results/plm/plr_ate_tune_coverage.csv b/results/plm/plr_ate_tune_coverage.csv index fa1f6276..cdc215b0 100644 --- a/results/plm/plr_ate_tune_coverage.csv +++ b/results/plm/plr_ate_tune_coverage.csv @@ -1,5 +1,5 @@ Learner g,Learner m,Score,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition -LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.805,0.14733663739723357,0.04709716445165978,1.2426113404810248,1.1115356809360686,200 -LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.845,0.14434980329449199,0.03915064046361367,1.1704317749756923,1.06174632188426,200 -LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.87,0.17556243192108348,0.04709716445165978,1.2426113404810248,1.1115356809360686,200 -LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.895,0.17200339957118416,0.03915064046361367,1.1704317749756923,1.06174632188426,200 +LGBM Regr.,LGBM Regr.,partialling out,0.9,False,0.794,0.14650747694301955,0.046075949286901785,1.2358182797669859,1.1165089778919557,500 +LGBM Regr.,LGBM Regr.,partialling out,0.9,True,0.862,0.1445322681554492,0.03843397524825611,1.169465886670257,1.0648564261804372,500 +LGBM Regr.,LGBM Regr.,partialling out,0.95,False,0.868,0.17457442630098685,0.046075949286901785,1.2358182797669859,1.1165089778919557,500 +LGBM Regr.,LGBM Regr.,partialling out,0.95,True,0.916,0.17222081986321527,0.03843397524825611,1.169465886670257,1.0648564261804372,500 diff --git a/results/plm/plr_ate_tune_metadata.csv b/results/plm/plr_ate_tune_metadata.csv index dafdeb83..be9e2842 100644 --- a/results/plm/plr_ate_tune_metadata.csv +++ b/results/plm/plr_ate_tune_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,PLRATETuningCoverageSimulation,2025-12-01 13:43,18.97071567773819,3.12.9,scripts/plm/plr_ate_tune_config.yml +0.12.dev0,PLRATETuningCoverageSimulation,2025-12-03 21:40,50.3229238708814,3.12.9,scripts/plm/plr_ate_tune_config.yml diff --git a/scripts/plm/plr_ate_tune_config.yml b/scripts/plm/plr_ate_tune_config.yml index 25bba462..df0ecd25 100644 --- a/scripts/plm/plr_ate_tune_config.yml +++ b/scripts/plm/plr_ate_tune_config.yml @@ -1,7 +1,7 @@ # Simulation parameters for PLR ATE Coverage simulation_parameters: - repetitions: 200 + repetitions: 500 max_runtime: 19800 # 5.5 hours in seconds random_seed: 42 n_jobs: -2 From be02667dceea6a26d81cbaa612c3d2349065b9b9 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 17:32:37 +0000 Subject: [PATCH 28/51] Update results from script: scripts/irm/iivm_late.py --- results/irm/iivm_late_coverage.csv | 32 +++++++++++++++--------------- results/irm/iivm_late_metadata.csv | 2 +- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/results/irm/iivm_late_coverage.csv b/results/irm/iivm_late_coverage.csv index b6e0e54c..1a1b1486 100644 --- a/results/irm/iivm_late_coverage.csv +++ b/results/irm/iivm_late_coverage.csv @@ -1,17 +1,17 @@ Learner g,Learner m,Learner r,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.946,1.1080585555024707,0.23427549254847976,1000 -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.978,1.3203332053146828,0.23427549254847976,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.9,0.943,1.1055825257787983,0.22791025413745555,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.95,0.972,1.3173828339238602,0.22791025413745555,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.9,0.932,1.0563347214139562,0.22752001300777,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.95,0.973,1.2587004555704382,0.22752001300777,1000 -LGBM Regr.,Logistic,Logistic,0.9,0.94,1.0505523267416146,0.22900092947174036,1000 -LGBM Regr.,Logistic,Logistic,0.95,0.974,1.2518103073429687,0.22900092947174036,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.948,1.0490801986799458,0.2195713594881401,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.981,1.2500561585638772,0.2195713594881401,1000 -LassoCV,LGBM Clas.,Logistic,0.9,0.946,1.0435082872686077,0.2207244658952354,1000 -LassoCV,LGBM Clas.,Logistic,0.95,0.98,1.2434168166112987,0.2207244658952354,1000 -LassoCV,Logistic,LGBM Clas.,0.9,0.935,1.0001394984833323,0.21177325003879846,1000 -LassoCV,Logistic,LGBM Clas.,0.95,0.973,1.191739717397429,0.21177325003879846,1000 -LassoCV,Logistic,Logistic,0.9,0.934,0.9931397101584005,0.21455142111202397,1000 -LassoCV,Logistic,Logistic,0.95,0.974,1.1833989551609148,0.21455142111202397,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.932,1.111672552573591,0.24712079914741866,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.966,1.324639548434561,0.24712079914741866,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.9,0.938,1.106712027476611,0.2448217653784481,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.95,0.974,1.318728718209195,0.2448217653784481,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.9,0.93,1.0567707396722865,0.24326330947680244,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.95,0.968,1.2592200033702707,0.24326330947680244,1000 +LGBM Regr.,Logistic,Logistic,0.9,0.928,1.0504737986107116,0.23894410467918908,1000 +LGBM Regr.,Logistic,Logistic,0.95,0.975,1.2517167353035965,0.23894410467918908,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.933,1.0512070933120594,0.2319586997131223,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.974,1.2525905098335282,0.2319586997131223,1000 +LassoCV,LGBM Clas.,Logistic,0.9,0.94,1.0461275433215658,0.22928895019032752,1000 +LassoCV,LGBM Clas.,Logistic,0.95,0.982,1.2465378526998414,0.22928895019032752,1000 +LassoCV,Logistic,LGBM Clas.,0.9,0.934,0.9969635378409042,0.22440759114740333,1000 +LassoCV,Logistic,LGBM Clas.,0.95,0.969,1.1879553268756944,0.22440759114740333,1000 +LassoCV,Logistic,Logistic,0.9,0.933,0.9899215135224229,0.21960950588542172,1000 +LassoCV,Logistic,Logistic,0.95,0.974,1.179564237348744,0.21960950588542172,1000 diff --git a/results/irm/iivm_late_metadata.csv b/results/irm/iivm_late_metadata.csv index 39f4b5b6..ff48a455 100644 --- a/results/irm/iivm_late_metadata.csv +++ b/results/irm/iivm_late_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IIVMLATECoverageSimulation,2025-09-08 07:01,23.067837047576905,3.12.3,scripts/irm/iivm_late_config.yml +0.12.dev0,IIVMLATECoverageSimulation,2025-12-04 17:32,22.869691892464957,3.12.3,scripts/irm/iivm_late_config.yml From b878e6131e0b6e8819ab63bd56d54be5695d77ee Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 17:47:13 +0000 Subject: [PATCH 29/51] Update results from script: scripts/irm/irm_atte_sensitivity.py --- results/irm/irm_atte_sensitivity_coverage.csv | 16 ++++++++-------- results/irm/irm_atte_sensitivity_metadata.csv | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/results/irm/irm_atte_sensitivity_coverage.csv b/results/irm/irm_atte_sensitivity_coverage.csv index ccd7784f..9c9f60dc 100644 --- a/results/irm/irm_atte_sensitivity_coverage.csv +++ b/results/irm/irm_atte_sensitivity_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Coverage (Lower),Coverage (Upper),RV,RVa,Bias (Lower),Bias (Upper),repetition -LGBM Regr.,LGBM Clas.,0.9,0.724,0.3486418874446855,0.133013559519858,0.956,1.0,0.10333425285677338,0.02230408488718529,0.061737229173530965,0.2573546286059918,500 -LGBM Regr.,LGBM Clas.,0.95,0.834,0.4154324322220136,0.133013559519858,0.984,1.0,0.10333425285677338,0.011131851516163303,0.061737229173530965,0.2573546286059918,500 -LGBM Regr.,Logistic,0.9,0.748,0.3463856879037955,0.12877617075802633,0.968,1.0,0.09674954143748549,0.019916333263196275,0.06074802565145977,0.25765051820022555,500 -LGBM Regr.,Logistic,0.95,0.852,0.41274400465033007,0.12877617075802633,0.988,1.0,0.09674954143748549,0.009890723264346906,0.06074802565145977,0.25765051820022555,500 -Linear,LGBM Clas.,0.9,0.784,0.3494413277277734,0.12187636804754974,0.966,1.0,0.09697064801116353,0.018075330967724065,0.06121749294728737,0.2428802817821339,500 -Linear,LGBM Clas.,0.95,0.878,0.4163850240739384,0.12187636804754974,0.988,1.0,0.09697064801116353,0.008509289290209597,0.06121749294728737,0.2428802817821339,500 -Linear,Logistic,0.9,0.94,0.35018867844196144,0.07142052633672426,0.998,1.0,0.055876995720152325,0.004611363724170049,0.09892318834816895,0.17357653591018374,500 -Linear,Logistic,0.95,0.97,0.4172755473762116,0.07142052633672426,1.0,1.0,0.055876995720152325,0.001736536891339805,0.09892318834816895,0.17357653591018374,500 +LGBM Regr.,LGBM Clas.,0.9,0.742,0.34905458483668395,0.13054186381999264,0.958,1.0,0.10160519674460072,0.02147924444029479,0.06225696870603676,0.2547167550221069,500 +LGBM Regr.,LGBM Clas.,0.95,0.848,0.4159241914956518,0.13054186381999264,0.988,1.0,0.10160519674460072,0.010345724353659373,0.06225696870603676,0.2547167550221069,500 +LGBM Regr.,Logistic,0.9,0.76,0.3468033024136178,0.1257451763861217,0.962,1.0,0.09464538506725147,0.019042064023498208,0.062099562490136685,0.2546157792911907,500 +LGBM Regr.,Logistic,0.95,0.87,0.4132416230312373,0.1257451763861217,0.99,1.0,0.09464538506725147,0.009057626091664835,0.062099562490136685,0.2546157792911907,500 +Linear,LGBM Clas.,0.9,0.8,0.3498779710118114,0.11925928428374606,0.962,1.0,0.09501448425797636,0.017305926960423847,0.06259908086771278,0.23917304693135916,500 +Linear,LGBM Clas.,0.95,0.888,0.4169053166378378,0.11925928428374606,0.99,1.0,0.09501448425797636,0.00818694195298031,0.06259908086771278,0.23917304693135916,500 +Linear,Logistic,0.9,0.954,0.35050172092922355,0.06846426368040737,1.0,1.0,0.05373903639592994,0.0038361798530380602,0.10030761571464483,0.17007849218927298,500 +Linear,Logistic,0.95,0.972,0.41764856050674876,0.06846426368040737,1.0,1.0,0.05373903639592994,0.0015020205315898524,0.10030761571464483,0.17007849218927298,500 diff --git a/results/irm/irm_atte_sensitivity_metadata.csv b/results/irm/irm_atte_sensitivity_metadata.csv index 10b3d82b..bb17a261 100644 --- a/results/irm/irm_atte_sensitivity_metadata.csv +++ b/results/irm/irm_atte_sensitivity_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMATTESensitivityCoverageSimulation,2025-09-08 07:16,37.43084245125453,3.12.3,scripts/irm/irm_atte_sensitivity_config.yml +0.12.dev0,IRMATTESensitivityCoverageSimulation,2025-12-04 17:47,37.48425861199697,3.12.3,scripts/irm/irm_atte_sensitivity_config.yml From 2b6a375036ce4c595b2cacef0be295bc7e8f2aa7 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 17:47:16 +0000 Subject: [PATCH 30/51] Update results from script: scripts/irm/irm_ate_sensitivity.py --- results/irm/irm_ate_sensitivity_coverage.csv | 16 ++++++++-------- results/irm/irm_ate_sensitivity_metadata.csv | 2 +- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/results/irm/irm_ate_sensitivity_coverage.csv b/results/irm/irm_ate_sensitivity_coverage.csv index ff0e59cf..60061b42 100644 --- a/results/irm/irm_ate_sensitivity_coverage.csv +++ b/results/irm/irm_ate_sensitivity_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Coverage (Lower),Coverage (Upper),RV,RVa,Bias (Lower),Bias (Upper),repetition -LGBM Regr.,LGBM Clas.,0.9,0.098,0.26691111802988843,0.18092094921744792,0.978,1.0,0.1251172046448644,0.05541109257381291,0.043433798604186634,0.32421141683166593,500 -LGBM Regr.,LGBM Clas.,0.95,0.24,0.31804421368572927,0.18092094921744792,0.998,1.0,0.1251172046448644,0.035581028493690985,0.043433798604186634,0.32421141683166593,500 -LGBM Regr.,Logistic,0.9,0.256,0.2575176748128544,0.15034061289364611,0.996,1.0,0.1012586582476983,0.035576767083884395,0.027405858296419432,0.29927796472935253,500 -LGBM Regr.,Logistic,0.95,0.526,0.3068512357243219,0.15034061289364611,1.0,1.0,0.1012586582476983,0.019006465720496604,0.027405858296419432,0.29927796472935253,500 -Linear,LGBM Clas.,0.9,0.102,0.2671222767999316,0.1797932596698173,0.97,1.0,0.1272534908574333,0.05595957612301405,0.045162467154599054,0.319626676739507,500 -Linear,LGBM Clas.,0.95,0.29,0.3182958248792866,0.1797932596698173,0.994,1.0,0.1272534908574333,0.035665775872197124,0.045162467154599054,0.319626676739507,500 -Linear,Logistic,0.9,0.86,0.25889374169956525,0.09086526017131878,1.0,1.0,0.06376487583520295,0.006934209791007705,0.05671789492555223,0.23636519842393028,500 -Linear,Logistic,0.95,0.96,0.30849092055346383,0.09086526017131878,1.0,1.0,0.06376487583520295,0.0017841395015015084,0.05671789492555223,0.23636519842393028,500 +LGBM Regr.,LGBM Clas.,0.9,0.104,0.26700744915395425,0.18155232199663573,0.962,1.0,0.12549287515490162,0.05598005111522917,0.044565302894415494,0.32482781026582425,500 +LGBM Regr.,LGBM Clas.,0.95,0.232,0.31815899929988095,0.18155232199663573,0.996,1.0,0.12549287515490162,0.03612029770121405,0.044565302894415494,0.32482781026582425,500 +LGBM Regr.,Logistic,0.9,0.23,0.257663233362731,0.15145245487884335,1.0,1.0,0.10207244929149722,0.03621321533456867,0.02593099116694401,0.3002768258881602,500 +LGBM Regr.,Logistic,0.95,0.522,0.30702467943428213,0.15145245487884335,1.0,1.0,0.10207244929149722,0.019182778604657814,0.02593099116694401,0.3002768258881602,500 +Linear,LGBM Clas.,0.9,0.108,0.26725519240961965,0.17907088100479915,0.974,1.0,0.12655801859027904,0.05541429018003078,0.04415911293427357,0.31913100847174025,500 +Linear,LGBM Clas.,0.95,0.27,0.318454203596823,0.17907088100479915,0.996,1.0,0.12655801859027904,0.03501281859485896,0.04415911293427357,0.31913100847174025,500 +Linear,Logistic,0.9,0.874,0.25906199626090154,0.09125618159082688,1.0,1.0,0.06408216867705586,0.006446037964926924,0.05593258931866834,0.23673871825981035,500 +Linear,Logistic,0.95,0.968,0.3086914082291149,0.09125618159082688,1.0,1.0,0.06408216867705586,0.0015805668774873335,0.05593258931866834,0.23673871825981035,500 diff --git a/results/irm/irm_ate_sensitivity_metadata.csv b/results/irm/irm_ate_sensitivity_metadata.csv index 6773dd4e..6e4c8479 100644 --- a/results/irm/irm_ate_sensitivity_metadata.csv +++ b/results/irm/irm_ate_sensitivity_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMATESensitivityCoverageSimulation,2025-09-08 07:16,37.67253222068151,3.12.3,scripts/irm/irm_ate_sensitivity_config.yml +0.12.dev0,IRMATESensitivityCoverageSimulation,2025-12-04 17:47,37.59833161433538,3.12.3,scripts/irm/irm_ate_sensitivity_config.yml From 0703b9a1c91da5ad52e34b306537bb057960aea3 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 18:24:41 +0000 Subject: [PATCH 31/51] Update results from script: scripts/irm/apo.py --- results/irm/apo_coverage.csv | 48 ++++++++++++++++++------------------ results/irm/apo_metadata.csv | 2 +- 2 files changed, 25 insertions(+), 25 deletions(-) diff --git a/results/irm/apo_coverage.csv b/results/irm/apo_coverage.csv index 3285fbf4..d65b7fe5 100644 --- a/results/irm/apo_coverage.csv +++ b/results/irm/apo_coverage.csv @@ -1,25 +1,25 @@ Learner g,Learner m,Treatment Level,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0,0.9,0.91,8.448708156821523,1.9750353954576516,1000 -LGBM Regr.,LGBM Clas.,0,0.95,0.964,10.06725670414229,1.9750353954576516,1000 -LGBM Regr.,LGBM Clas.,1,0.9,0.929,33.97368183551072,8.24578707402124,1000 -LGBM Regr.,LGBM Clas.,1,0.95,0.972,40.48213879263808,8.24578707402124,1000 -LGBM Regr.,LGBM Clas.,2,0.9,0.892,32.60041903902424,8.57187519908264,1000 -LGBM Regr.,LGBM Clas.,2,0.95,0.962,38.845795243084254,8.57187519908264,1000 -LGBM Regr.,Logistic,0,0.9,0.919,5.622417369664611,1.3041095784829069,1000 -LGBM Regr.,Logistic,0,0.95,0.966,6.699523513845268,1.3041095784829069,1000 -LGBM Regr.,Logistic,1,0.9,0.919,7.087080260443338,1.6525542055578457,1000 -LGBM Regr.,Logistic,1,0.95,0.956,8.444776993171013,1.6525542055578457,1000 -LGBM Regr.,Logistic,2,0.9,0.919,7.122040852887705,1.617065797288604,1000 -LGBM Regr.,Logistic,2,0.95,0.965,8.486435108486805,1.617065797288604,1000 -Linear,LGBM Clas.,0,0.9,0.912,5.4550159030998895,1.2942930123955605,1000 -Linear,LGBM Clas.,0,0.95,0.96,6.50005236331248,1.2942930123955605,1000 -Linear,LGBM Clas.,1,0.9,0.956,9.813026810453364,2.0391427947858425,1000 -Linear,LGBM Clas.,1,0.95,0.98,11.692942650137699,2.0391427947858425,1000 -Linear,LGBM Clas.,2,0.9,0.931,7.129870255664527,1.5863997469468463,1000 -Linear,LGBM Clas.,2,0.95,0.97,8.495764417315014,1.5863997469468463,1000 -Linear,Logistic,0,0.9,0.916,5.337733090643185,1.2727441530326435,1000 -Linear,Logistic,0,0.95,0.961,6.36030127260495,1.2727441530326435,1000 -Linear,Logistic,1,0.9,0.916,5.417715726893596,1.2843274350382277,1000 -Linear,Logistic,1,0.95,0.96,6.455606461997341,1.2843274350382277,1000 -Linear,Logistic,2,0.9,0.909,5.365443726631665,1.277028275846632,1000 -Linear,Logistic,2,0.95,0.959,6.393320531970161,1.277028275846632,1000 +LGBM Regr.,LGBM Clas.,0,0.9,0.911,8.367147388814955,1.9468891829084087,1000 +LGBM Regr.,LGBM Clas.,0,0.95,0.959,9.970071054778114,1.9468891829084087,1000 +LGBM Regr.,LGBM Clas.,1,0.9,0.927,33.90278245856187,8.08626102965796,1000 +LGBM Regr.,LGBM Clas.,1,0.95,0.963,40.397656974274945,8.08626102965796,1000 +LGBM Regr.,LGBM Clas.,2,0.9,0.912,33.28384710426898,8.353006249801732,1000 +LGBM Regr.,LGBM Clas.,2,0.95,0.963,39.660150011165456,8.353006249801732,1000 +LGBM Regr.,Logistic,0,0.9,0.912,5.63730833179423,1.3144102702189047,1000 +LGBM Regr.,Logistic,0,0.95,0.955,6.717267189629536,1.3144102702189047,1000 +LGBM Regr.,Logistic,1,0.9,0.92,7.122502980817966,1.6196432403482612,1000 +LGBM Regr.,Logistic,1,0.95,0.969,8.486985767879665,1.6196432403482612,1000 +LGBM Regr.,Logistic,2,0.9,0.924,7.076807184165477,1.558379920402528,1000 +LGBM Regr.,Logistic,2,0.95,0.959,8.43253586776926,1.558379920402528,1000 +Linear,LGBM Clas.,0,0.9,0.906,5.4628463152959785,1.2764446154981781,1000 +Linear,LGBM Clas.,0,0.95,0.949,6.509382874937932,1.2764446154981781,1000 +Linear,LGBM Clas.,1,0.9,0.939,9.808324498456713,2.085746167612665,1000 +Linear,LGBM Clas.,1,0.95,0.977,11.687339499798673,2.085746167612665,1000 +Linear,LGBM Clas.,2,0.9,0.934,7.176220883961563,1.5650173182054261,1000 +Linear,LGBM Clas.,2,0.95,0.975,8.550994597456553,1.5650173182054261,1000 +Linear,Logistic,0,0.9,0.906,5.3483608617226395,1.2574610450449657,1000 +Linear,Logistic,0,0.95,0.953,6.372965042930987,1.2574610450449657,1000 +Linear,Logistic,1,0.9,0.9,5.425955174051074,1.283578434674604,1000 +Linear,Logistic,1,0.95,0.954,6.465424368841191,1.283578434674604,1000 +Linear,Logistic,2,0.9,0.914,5.377402065139319,1.246610508634217,1000 +Linear,Logistic,2,0.95,0.952,6.407569771176558,1.246610508634217,1000 diff --git a/results/irm/apo_metadata.csv b/results/irm/apo_metadata.csv index c159f457..f79f3193 100644 --- a/results/irm/apo_metadata.csv +++ b/results/irm/apo_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,APOCoverageSimulation,2025-09-08 07:52,74.02908265988032,3.12.3,scripts/irm/apo_config.yml +0.12.dev0,APOCoverageSimulation,2025-12-04 18:24,75.00107036828994,3.12.3,scripts/irm/apo_config.yml From 883c3161c8e714ce80bc8e4a10218e24d8aade3d Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 18:24:09 +0000 Subject: [PATCH 32/51] Update results from script: scripts/irm/apos.py --- results/irm/apos_causal_contrast.csv | 16 ++++++++-------- results/irm/apos_coverage.csv | 16 ++++++++-------- results/irm/apos_metadata.csv | 2 +- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/results/irm/apos_causal_contrast.csv b/results/irm/apos_causal_contrast.csv index 149a0532..ce3c2b6f 100644 --- a/results/irm/apos_causal_contrast.csv +++ b/results/irm/apos_causal_contrast.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,0.8985,33.60313998768328,8.62676858480036,0.915,39.77456075827479,1000 -LGBM Regr.,LGBM Clas.,0.95,0.959,40.040610948088954,8.62676858480036,0.972,45.62380258315557,1000 -LGBM Regr.,Logistic,0.9,0.942,5.3683923473672825,1.1064466312245236,0.949,6.347349786459089,1000 -LGBM Regr.,Logistic,0.95,0.975,6.396834030284652,1.1064466312245236,0.98,7.289008003904023,1000 -Linear,LGBM Clas.,0.9,0.963,6.610619627981532,1.2930291385928911,0.97,7.817833271049541,1000 -Linear,LGBM Clas.,0.95,0.9845,7.877039132260507,1.2930291385928911,0.989,8.965895782069206,1000 -Linear,Logistic,0.9,0.88,1.1440244887213769,0.29717144124251454,0.862,1.3513672686392388,1000 -Linear,Logistic,0.95,0.9335,1.3631892580505587,0.29717144124251454,0.938,1.551247330789396,1000 +LGBM Regr.,LGBM Clas.,0.9,0.908,33.52458756996381,8.669768972982405,0.912,39.69038177637447,1000 +LGBM Regr.,LGBM Clas.,0.95,0.956,39.947009969189665,8.669768972982405,0.963,45.539207400880635,1000 +LGBM Regr.,Logistic,0.9,0.942,5.418376815341814,1.1177578246481668,0.951,6.413959977441442,1000 +LGBM Regr.,Logistic,0.95,0.975,6.456394197469891,1.1177578246481668,0.979,7.3596604350528905,1000 +Linear,LGBM Clas.,0.9,0.958,6.645789732664919,1.3068883576166876,0.968,7.873338052971561,1000 +Linear,LGBM Clas.,0.95,0.9845,7.918946896807132,1.3068883576166876,0.989,9.033483053185353,1000 +Linear,Logistic,0.9,0.896,1.1481321709355257,0.2853591542610903,0.885,1.357216597612154,1000 +Linear,Logistic,0.95,0.9435,1.368083863301598,0.2853591542610903,0.932,1.5583287806380506,1000 diff --git a/results/irm/apos_coverage.csv b/results/irm/apos_coverage.csv index c5cb9271..9a1668bb 100644 --- a/results/irm/apos_coverage.csv +++ b/results/irm/apos_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,0.9116666666666666,25.380884756214634,6.3033576983720385,0.931,32.56526868720959,1000 -LGBM Regr.,LGBM Clas.,0.95,0.9633333333333334,30.243189547594916,6.3033576983720385,0.971,36.806047640863575,1000 -LGBM Regr.,Logistic,0.9,0.9153333333333333,6.6236336046586155,1.5450159773054415,0.923,8.149672568213141,1000 -LGBM Regr.,Logistic,0.95,0.9576666666666667,7.892546241929591,1.5450159773054415,0.959,9.334305451163466,1000 -Linear,LGBM Clas.,0.9,0.9206666666666666,7.481260581637696,1.6455175148624328,0.932,9.256732930958306,1000 -Linear,LGBM Clas.,0.95,0.9673333333333334,8.914471816039386,1.6455175148624328,0.965,10.567345838624604,1000 -Linear,Logistic,0.9,0.8983333333333333,5.379448642069803,1.3088594234228312,0.9,5.8038157310767735,1000 -Linear,Logistic,0.95,0.9453333333333334,6.41000841800179,1.3088594234228312,0.947,6.826689793179672,1000 +LGBM Regr.,LGBM Clas.,0.9,0.911,25.33383305218413,6.362966930836552,0.937,32.467609705425545,1000 +LGBM Regr.,LGBM Clas.,0.95,0.9613333333333334,30.1871239841916,6.362966930836552,0.975,36.70233354816845,1000 +LGBM Regr.,Logistic,0.9,0.9023333333333333,6.668208361349107,1.5732731164170273,0.905,8.201551480986833,1000 +LGBM Regr.,Logistic,0.95,0.9503333333333334,7.945660340534777,1.5732731164170273,0.954,9.38621417059308,1000 +Linear,LGBM Clas.,0.9,0.916,7.5097289653187325,1.7065530275498664,0.928,9.289774829157478,1000 +Linear,LGBM Clas.,0.95,0.9626666666666667,8.94839398747072,1.7065530275498664,0.965,10.610827335816024,1000 +Linear,Logistic,0.9,0.8946666666666666,5.395004545334627,1.319774939600406,0.887,5.817098751565451,1000 +Linear,Logistic,0.95,0.9476666666666667,6.428544420018306,1.319774939600406,0.945,6.848624032317537,1000 diff --git a/results/irm/apos_metadata.csv b/results/irm/apos_metadata.csv index bea57944..a1ec5ca9 100644 --- a/results/irm/apos_metadata.csv +++ b/results/irm/apos_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,APOSCoverageSimulation,2025-09-08 07:52,73.54105892578761,3.12.3,scripts/irm/apos_config.yml +0.12.dev0,APOSCoverageSimulation,2025-12-04 18:24,74.42682578563691,3.12.3,scripts/irm/apos_config.yml From 10de76de523bcb0e39d9f85527f6cbb3497361d7 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 18:30:49 +0000 Subject: [PATCH 33/51] Update results from script: scripts/irm/irm_gate.py --- results/irm/irm_gate_coverage.csv | 28 ++++++++++++++-------------- results/irm/irm_gate_metadata.csv | 2 +- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/results/irm/irm_gate_coverage.csv b/results/irm/irm_gate_coverage.csv index 51ccc2c7..0c584acf 100644 --- a/results/irm/irm_gate_coverage.csv +++ b/results/irm/irm_gate_coverage.csv @@ -1,15 +1,15 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,0.9253333333333333,0.8534850261615968,0.19518739930256998,1.0,2.0057889835769687,1000 -LGBM Regr.,LGBM Clas.,0.95,0.9723333333333334,1.01699013529932,0.19518739930256998,1.0,2.008734669100397,1000 -LGBM Regr.,Logistic,0.9,0.907,0.40290561820602877,0.09637183426509835,0.997,0.9452563591757599,1000 -LGBM Regr.,Logistic,0.95,0.9516666666666667,0.4800916555208833,0.09637183426509835,0.997,0.9479550579138624,1000 -Linear,LGBM Clas.,0.9,0.925,0.8587043448718052,0.19393419957109048,1.0,2.0174488850580667,1000 -Linear,LGBM Clas.,0.95,0.968,1.0232093371348077,0.19393419957109048,1.0,2.0305155614489188,1000 -Linear,Logistic,0.9,0.9093333333333333,0.420378374218193,0.09849785707055471,1.0,0.9913489475406314,1000 -Linear,Logistic,0.95,0.957,0.5009117284643757,0.09849785707055471,1.0,0.9861142484974326,1000 -Linear,RF Clas.,0.9,0.9176666666666666,0.44418271473286164,0.10211946295839633,1.0,1.0448891408997685,1000 -Linear,RF Clas.,0.95,0.9566666666666667,0.5292763496805192,0.10211946295839633,0.999,1.0422977378868823,1000 -RF Regr.,Logistic,0.9,0.9036666666666666,0.40328321693084784,0.09651797231475077,0.999,0.9482063865240357,1000 -RF Regr.,Logistic,0.95,0.9463333333333334,0.4805415921530111,0.09651797231475077,0.998,0.9478627389064088,1000 -RF Regr.,RF Clas.,0.9,0.9126666666666666,0.4265183348447747,0.1006978867872414,0.999,1.0031066471442747,1000 -RF Regr.,RF Clas.,0.95,0.955,0.5082279428055252,0.1006978867872414,1.0,1.0042312153427795,1000 +LGBM Regr.,LGBM Clas.,0.9,0.9386666666666666,0.8559513177443687,0.1937643264532876,1.0,2.006280185703383,1000 +LGBM Regr.,LGBM Clas.,0.95,0.9703333333333334,1.0199289029795582,0.1937643264532876,0.999,2.0155442343424883,1000 +LGBM Regr.,Logistic,0.9,0.9066666666666666,0.40149436064143407,0.09408161185194555,0.999,0.9449920639738041,1000 +LGBM Regr.,Logistic,0.95,0.947,0.4784100384127144,0.09408161185194555,0.997,0.9432948190496762,1000 +Linear,LGBM Clas.,0.9,0.916,0.8565340550805809,0.19475726876642258,1.0,2.0099798454237723,1000 +Linear,LGBM Clas.,0.95,0.9683333333333334,1.0206232773437618,0.19475726876642258,1.0,2.012306077187581,1000 +Linear,Logistic,0.9,0.9096666666666666,0.4187055393390181,0.09576110520986783,0.998,0.9871284997732542,1000 +Linear,Logistic,0.95,0.9573333333333334,0.49891842276133763,0.09576110520986783,0.998,0.9832531417484586,1000 +Linear,RF Clas.,0.9,0.912,0.4432050057206287,0.10020711620099187,0.998,1.0443477449082788,1000 +Linear,RF Clas.,0.95,0.96,0.528111337536011,0.10020711620099187,0.998,1.043185424691669,1000 +RF Regr.,Logistic,0.9,0.909,0.40204415185082115,0.09401292200323333,0.997,0.9441304263307014,1000 +RF Regr.,Logistic,0.95,0.9513333333333334,0.4790651550454403,0.09401292200323333,0.998,0.9459424215336382,1000 +RF Regr.,RF Clas.,0.9,0.9106666666666666,0.4240128937078121,0.09786079181797877,0.999,0.999003996144668,1000 +RF Regr.,RF Clas.,0.95,0.9546666666666667,0.5052425255541841,0.09786079181797877,0.999,0.9978199943917729,1000 diff --git a/results/irm/irm_gate_metadata.csv b/results/irm/irm_gate_metadata.csv index d9aa4051..81261d42 100644 --- a/results/irm/irm_gate_metadata.csv +++ b/results/irm/irm_gate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMGATECoverageSimulation,2025-09-08 07:57,78.74852496782938,3.12.3,scripts/irm/irm_gate_config.yml +0.12.dev0,IRMGATECoverageSimulation,2025-12-04 18:30,81.06913734674454,3.12.3,scripts/irm/irm_gate_config.yml From 0814dd63f51fa254f4763fbd9201d4eb1406ddb3 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 18:31:35 +0000 Subject: [PATCH 34/51] Update results from script: scripts/irm/irm_cate.py --- results/irm/irm_cate_coverage.csv | 28 ++++++++++++++-------------- results/irm/irm_cate_metadata.csv | 2 +- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/results/irm/irm_cate_coverage.csv b/results/irm/irm_cate_coverage.csv index 707a3fc6..7dc3a803 100644 --- a/results/irm/irm_cate_coverage.csv +++ b/results/irm/irm_cate_coverage.csv @@ -1,15 +1,15 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,0.92618,1.0549550417797375,0.24677433876498978,1.0,2.655295049518824,1000 -LGBM Regr.,LGBM Clas.,0.95,0.96976,1.2570564658871226,0.24677433876498978,1.0,2.6579031332044774,1000 -LGBM Regr.,Logistic,0.9,0.89558,0.460768694911557,0.11272247714552291,0.996,1.15553358576414,1000 -LGBM Regr.,Logistic,0.95,0.94716,0.5490397640451075,0.11272247714552291,0.998,1.1555911642405947,1000 -Linear,LGBM Clas.,0.9,0.9045700000000001,1.049455728670136,0.2545832624791602,1.0,2.641712139711077,1000 -Linear,LGBM Clas.,0.95,0.9562999999999999,1.2505036301466534,0.2545832624791602,1.0,2.6340664078152676,1000 -Linear,Logistic,0.9,0.89837,0.47635891687924603,0.11622820700106382,0.999,1.1994653037592913,1000 -Linear,Logistic,0.95,0.94899,0.5676166593183287,0.11622820700106382,1.0,1.202613136922296,1000 -Linear,RF Clas.,0.9,0.90583,0.5108385409019408,0.12273855836370781,0.999,1.2859328834061883,1000 -Linear,RF Clas.,0.95,0.95561,0.608701665411067,0.12273855836370781,0.999,1.290594144217973,1000 -RF Regr.,Logistic,0.9,0.89388,0.46090264991879476,0.11328948669625669,1.0,1.1580571746486201,1000 -RF Regr.,Logistic,0.95,0.94721,0.5491993812812146,0.11328948669625669,0.999,1.1597345940806152,1000 -RF Regr.,RF Clas.,0.9,0.8976799999999999,0.4952927246994588,0.12057106303154265,1.0,1.2461592875136045,1000 -RF Regr.,RF Clas.,0.95,0.94829,0.5901776828706784,0.12057106303154265,0.999,1.245662595805751,1000 +LGBM Regr.,LGBM Clas.,0.9,0.92461,1.0665951249223096,0.24613991582170225,1.0,2.6653759321537116,1000 +LGBM Regr.,LGBM Clas.,0.95,0.97023,1.2709264804359406,0.24613991582170225,1.0,2.6812445695035247,1000 +LGBM Regr.,Logistic,0.9,0.89795,0.4614986062328707,0.11130386458867365,0.999,1.1580110182466605,1000 +LGBM Regr.,Logistic,0.95,0.94769,0.5499095070290679,0.11130386458867365,1.0,1.1604523683663983,1000 +Linear,LGBM Clas.,0.9,0.90908,1.0653909229898018,0.2559912909682445,1.0,2.6806660530315596,1000 +Linear,LGBM Clas.,0.95,0.9567,1.2694915853308952,0.2559912909682445,0.999,2.680517403176158,1000 +Linear,Logistic,0.9,0.90534,0.4776874641392367,0.11362720144689947,1.0,1.2021014808982184,1000 +Linear,Logistic,0.95,0.9505399999999999,0.5691997210197929,0.11362720144689947,0.996,1.2007226696609286,1000 +Linear,RF Clas.,0.9,0.91122,0.5134643281598085,0.11906161888403807,1.0,1.2933158587254148,1000 +Linear,RF Clas.,0.95,0.95521,0.6118304839102687,0.11906161888403807,1.0,1.2923682725270258,1000 +RF Regr.,Logistic,0.9,0.89883,0.4617571633800954,0.11143886684767142,0.997,1.165093904354102,1000 +RF Regr.,Logistic,0.95,0.94936,0.5502175968725668,0.11143886684767142,0.998,1.1641657405751544,1000 +RF Regr.,RF Clas.,0.9,0.9018999999999999,0.49638796465577406,0.11812020692019015,1.0,1.2489623144139728,1000 +RF Regr.,RF Clas.,0.95,0.94932,0.5914827417729625,0.11812020692019015,0.999,1.2486145265039033,1000 diff --git a/results/irm/irm_cate_metadata.csv b/results/irm/irm_cate_metadata.csv index abbba129..efbfdb58 100644 --- a/results/irm/irm_cate_metadata.csv +++ b/results/irm/irm_cate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMCATECoverageSimulation,2025-09-08 07:57,79.25359026590984,3.12.3,scripts/irm/irm_cate_config.yml +0.12.dev0,IRMCATECoverageSimulation,2025-12-04 18:31,81.88368842999141,3.12.3,scripts/irm/irm_cate_config.yml From 9281637083a9ce607996ba436c06187a949e447d Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 18:37:14 +0000 Subject: [PATCH 35/51] Update results from script: scripts/irm/cvar.py --- results/irm/cvar_Y0_coverage.csv | 16 ++++++++-------- results/irm/cvar_Y1_coverage.csv | 16 ++++++++-------- results/irm/cvar_effect_coverage.csv | 16 ++++++++-------- results/irm/cvar_metadata.csv | 2 +- 4 files changed, 25 insertions(+), 25 deletions(-) diff --git a/results/irm/cvar_Y0_coverage.csv b/results/irm/cvar_Y0_coverage.csv index d982b134..e6bac3da 100644 --- a/results/irm/cvar_Y0_coverage.csv +++ b/results/irm/cvar_Y0_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,0.7992857142857143,0.5698895627940845,0.1622318401991349,200 -LGBM Regr.,LGBM Clas.,0.95,0.8728571428571429,0.6790652979328213,0.1622318401991349,200 -LGBM Regr.,Logistic,0.9,0.7892857142857143,0.444416645306349,0.13289988655229823,200 -LGBM Regr.,Logistic,0.95,0.8857142857142857,0.5295550951514872,0.13289988655229823,200 -Linear,LGBM Clas.,0.9,0.8221428571428571,0.5759694248495425,0.16067305644701288,200 -Linear,LGBM Clas.,0.95,0.88,0.6863099004095503,0.16067305644701288,200 -Linear,Logistic,0.9,0.77,0.4638841356789262,0.14556325752609944,200 -Linear,Logistic,0.95,0.8385714285714286,0.5527520406878192,0.14556325752609944,200 +LGBM Regr.,LGBM Clas.,0.9,0.8135714285714286,0.558683997772339,0.16093846082916122,200 +LGBM Regr.,LGBM Clas.,0.95,0.8771428571428571,0.6657130436597479,0.16093846082916122,200 +LGBM Regr.,Logistic,0.9,0.83,0.4284775657171733,0.1276714757715089,200 +LGBM Regr.,Logistic,0.95,0.8771428571428571,0.5105625103830777,0.1276714757715089,200 +Linear,LGBM Clas.,0.9,0.7892857142857143,0.5630811366272137,0.1750343717735893,200 +Linear,LGBM Clas.,0.95,0.8492857142857143,0.6709525577717416,0.1750343717735893,200 +Linear,Logistic,0.9,0.7421428571428571,0.45008918626971695,0.14782345478591596,200 +Linear,Logistic,0.95,0.835,0.5363143446110485,0.14782345478591596,200 diff --git a/results/irm/cvar_Y1_coverage.csv b/results/irm/cvar_Y1_coverage.csv index 1b724efb..34a99f53 100644 --- a/results/irm/cvar_Y1_coverage.csv +++ b/results/irm/cvar_Y1_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,0.917142857142857,0.19119717658041008,0.04362829784730854,200 -LGBM Regr.,LGBM Clas.,0.95,0.9542857142857143,0.22782548787510115,0.04362829784730854,200 -LGBM Regr.,Logistic,0.9,0.9057142857142857,0.1808152569639124,0.04420216660189615,200 -LGBM Regr.,Logistic,0.95,0.9464285714285714,0.21545466763595436,0.04420216660189615,200 -Linear,LGBM Clas.,0.9,0.9307142857142857,0.21249094126531845,0.04611913119447561,200 -Linear,LGBM Clas.,0.95,0.9685714285714286,0.2531985734760624,0.04611913119447561,200 -Linear,Logistic,0.9,0.907142857142857,0.1974588750818297,0.04611240388345785,200 -Linear,Logistic,0.95,0.9592857142857143,0.2352867618412089,0.04611240388345785,200 +LGBM Regr.,LGBM Clas.,0.9,0.9314285714285714,0.19151734578017915,0.04104704711866214,200 +LGBM Regr.,LGBM Clas.,0.95,0.9657142857142857,0.22820699300736602,0.04104704711866214,200 +LGBM Regr.,Logistic,0.9,0.9314285714285714,0.18081183530929518,0.039318346927923055,200 +LGBM Regr.,Logistic,0.95,0.9664285714285714,0.21545059048300444,0.039318346927923055,200 +Linear,LGBM Clas.,0.9,0.9514285714285714,0.21336023206199958,0.042369199648548656,200 +Linear,LGBM Clas.,0.95,0.9764285714285714,0.25423439734857617,0.042369199648548656,200 +Linear,Logistic,0.9,0.9285714285714286,0.1967447832942214,0.041760333368338426,200 +Linear,Logistic,0.95,0.9735714285714286,0.2344358689943104,0.041760333368338426,200 diff --git a/results/irm/cvar_effect_coverage.csv b/results/irm/cvar_effect_coverage.csv index c2c9d886..913c2581 100644 --- a/results/irm/cvar_effect_coverage.csv +++ b/results/irm/cvar_effect_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Clas.,0.9,0.8285714285714286,0.5820773835982563,0.16340146510609896,0.8,0.7041248792060457,200 -LGBM Regr.,LGBM Clas.,0.95,0.8828571428571429,0.6935879821612518,0.16340146510609896,0.865,0.8104981741914127,200 -LGBM Regr.,Logistic,0.9,0.8078571428571429,0.4557462112689952,0.13573460597377687,0.775,0.5481526458665991,200 -LGBM Regr.,Logistic,0.95,0.8935714285714286,0.5430551056590559,0.13573460597377687,0.875,0.632067835425355,200 -Linear,LGBM Clas.,0.9,0.827857142857143,0.6010696922562259,0.16176034889643487,0.795,0.7100199778744414,200 -Linear,LGBM Clas.,0.95,0.8864285714285713,0.7162187137612902,0.16176034889643487,0.855,0.8211084080550509,200 -Linear,Logistic,0.9,0.7728571428571429,0.48825700023292934,0.14657921460513024,0.755,0.5721453699087213,200 -Linear,Logistic,0.95,0.8542857142857143,0.5817941000803347,0.14657921460513024,0.83,0.662941047401618,200 +LGBM Regr.,LGBM Clas.,0.9,0.827857142857143,0.5710685305481125,0.16052920368203474,0.785,0.6906174048262292,200 +LGBM Regr.,LGBM Clas.,0.95,0.8885714285714286,0.6804701246596295,0.16052920368203474,0.895,0.7953244912486926,200 +LGBM Regr.,Logistic,0.9,0.8271428571428571,0.43999562239145346,0.12430365207842994,0.815,0.5327747575457736,200 +LGBM Regr.,Logistic,0.95,0.8892857142857143,0.5242871214266269,0.12430365207842994,0.87,0.6147424507415543,200 +Linear,LGBM Clas.,0.9,0.7892857142857143,0.5891418372517685,0.18374743604747235,0.745,0.6976304863416719,200 +Linear,LGBM Clas.,0.95,0.8621428571428571,0.7020057978893286,0.18374743604747235,0.84,0.8072538231232462,200 +Linear,Logistic,0.9,0.7707142857142857,0.47465066399401157,0.15252675761847112,0.735,0.5599340403260908,200 +Linear,Logistic,0.95,0.8342857142857143,0.5655811504580351,0.15252675761847112,0.8,0.6483698794785692,200 diff --git a/results/irm/cvar_metadata.csv b/results/irm/cvar_metadata.csv index 7520a27c..37e79372 100644 --- a/results/irm/cvar_metadata.csv +++ b/results/irm/cvar_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,CVARCoverageSimulation,2025-09-08 08:12,94.27919102907181,3.12.3,scripts/irm/cvar_config.yml +0.12.dev0,CVARCoverageSimulation,2025-12-04 18:37,87.47529164155324,3.12.3,scripts/irm/cvar_config.yml From df8ffeb4228c53540a571a0736e5f8dd734204f1 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:04:29 +0000 Subject: [PATCH 36/51] Update results from script: scripts/irm/lpq.py --- results/irm/lpq_Y0_coverage.csv | 16 ++++++++-------- results/irm/lpq_Y1_coverage.csv | 16 ++++++++-------- results/irm/lpq_effect_coverage.csv | 16 ++++++++-------- results/irm/lpq_metadata.csv | 2 +- 4 files changed, 25 insertions(+), 25 deletions(-) diff --git a/results/irm/lpq_Y0_coverage.csv b/results/irm/lpq_Y0_coverage.csv index eae541b7..b23433be 100644 --- a/results/irm/lpq_Y0_coverage.csv +++ b/results/irm/lpq_Y0_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Clas.,LGBM Clas.,0.9,0.9329999999999999,1.1960147129034748,0.24094548048195796,200 -LGBM Clas.,LGBM Clas.,0.95,0.972,1.425139431169569,0.24094548048195796,200 -LGBM Clas.,Logistic,0.9,0.932,1.1491710932229942,0.2296474103759401,200 -LGBM Clas.,Logistic,0.95,0.97,1.3693218155624007,0.2296474103759401,200 -Logistic,LGBM Clas.,0.9,0.9359999999999999,1.1629913431768941,0.22567871572576473,200 -Logistic,LGBM Clas.,0.95,0.968,1.3857896590976262,0.22567871572576473,200 -Logistic,Logistic,0.9,0.934,1.1208189409885447,0.22329276488816077,200 -Logistic,Logistic,0.95,0.9690000000000001,1.3355381424420707,0.22329276488816077,200 +LGBM Clas.,LGBM Clas.,0.9,0.9329999999999999,1.1918146610913505,0.23713567415485812,200 +LGBM Clas.,LGBM Clas.,0.95,0.965,1.420134760753866,0.23713567415485812,200 +LGBM Clas.,Logistic,0.9,0.935,1.1482506900557687,0.22706213155177937,200 +LGBM Clas.,Logistic,0.95,0.9690000000000001,1.368225087543895,0.22706213155177937,200 +Logistic,LGBM Clas.,0.9,0.937,1.1636475947083718,0.222049794644765,200 +Logistic,LGBM Clas.,0.95,0.97,1.3865716310283926,0.222049794644765,200 +Logistic,Logistic,0.9,0.9390000000000001,1.1179439847426047,0.21856700650754168,200 +Logistic,Logistic,0.95,0.973,1.3321124207809802,0.21856700650754168,200 diff --git a/results/irm/lpq_Y1_coverage.csv b/results/irm/lpq_Y1_coverage.csv index 5524bf55..8fe08a13 100644 --- a/results/irm/lpq_Y1_coverage.csv +++ b/results/irm/lpq_Y1_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Clas.,LGBM Clas.,0.9,0.935,1.678879388568732,0.31691835808807167,200 -LGBM Clas.,LGBM Clas.,0.95,0.965,2.0005081802202342,0.31691835808807167,200 -LGBM Clas.,Logistic,0.9,0.935,1.621053614944002,0.309555060210687,200 -LGBM Clas.,Logistic,0.95,0.966,1.93160452105836,0.309555060210687,200 -Logistic,LGBM Clas.,0.9,0.9279999999999999,1.6303000750790089,0.3127093206367988,200 -Logistic,LGBM Clas.,0.95,0.9590000000000001,1.9426223578750537,0.3127093206367988,200 -Logistic,Logistic,0.9,0.932,1.5733563331643035,0.2975937700943579,200 -Logistic,Logistic,0.95,0.965,1.874769704320332,0.2975937700943579,200 +LGBM Clas.,LGBM Clas.,0.9,0.938,1.6625528920375123,0.32490050446017593,200 +LGBM Clas.,LGBM Clas.,0.95,0.973,1.9810539596922865,0.32490050446017593,200 +LGBM Clas.,Logistic,0.9,0.9390000000000001,1.6016687007398658,0.29732874610502585,200 +LGBM Clas.,Logistic,0.95,0.973,1.9085059710956365,0.29732874610502585,200 +Logistic,LGBM Clas.,0.9,0.941,1.608047549586303,0.30902052918537687,200 +Logistic,LGBM Clas.,0.95,0.9690000000000001,1.9161068383077622,0.30902052918537687,200 +Logistic,Logistic,0.9,0.935,1.555801006941688,0.29641972770100156,200 +Logistic,Logistic,0.95,0.973,1.8538512429026142,0.29641972770100156,200 diff --git a/results/irm/lpq_effect_coverage.csv b/results/irm/lpq_effect_coverage.csv index c76f5320..ddc6e6f4 100644 --- a/results/irm/lpq_effect_coverage.csv +++ b/results/irm/lpq_effect_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Clas.,LGBM Clas.,0.9,0.907,1.6633268414956404,0.3832694765515751,0.885,2.195582944870515,200 -LGBM Clas.,LGBM Clas.,0.95,0.9470000000000001,1.981976177352831,0.3832694765515751,0.935,2.4824600181934997,200 -LGBM Clas.,Logistic,0.9,0.9009999999999999,1.6061436270948157,0.36919254431923265,0.9,2.1168287667046233,200 -LGBM Clas.,Logistic,0.95,0.9440000000000001,1.9138381747309388,0.36919254431923265,0.94,2.3959456398753134,200 -Logistic,LGBM Clas.,0.9,0.89,1.6221649413900492,0.384184011499731,0.875,2.130874728171255,200 -Logistic,LGBM Clas.,0.95,0.937,1.9329287481954314,0.384184011499731,0.93,2.41312138531884,200 -Logistic,Logistic,0.9,0.888,1.5652859556745662,0.3790378210858529,0.87,2.052704952998973,200 -Logistic,Logistic,0.95,0.93,1.8651532564113202,0.3790378210858529,0.945,2.3240450427903903,200 +LGBM Clas.,LGBM Clas.,0.9,0.905,1.646407440278241,0.385814947577832,0.905,2.1721384098939738,200 +LGBM Clas.,LGBM Clas.,0.95,0.95,1.9618154673159467,0.385814947577832,0.94,2.454333240166447,200 +LGBM Clas.,Logistic,0.9,0.889,1.5870851465992528,0.36383204534825536,0.905,2.091555216794098,200 +LGBM Clas.,Logistic,0.95,0.946,1.8911285945231282,0.36383204534825536,0.94,2.3668596812405753,200 +Logistic,LGBM Clas.,0.9,0.895,1.602525995092381,0.36399924087424396,0.905,2.1078231981172495,200 +Logistic,LGBM Clas.,0.95,0.951,1.909527500323251,0.36399924087424396,0.955,2.3859405915685907,200 +Logistic,Logistic,0.9,0.89,1.5476210853564174,0.3610200075249385,0.89,2.0288270162637994,200 +Logistic,Logistic,0.95,0.9420000000000001,1.8441042651528634,0.3610200075249385,0.95,2.300361347965683,200 diff --git a/results/irm/lpq_metadata.csv b/results/irm/lpq_metadata.csv index 05c3eb6a..e679db01 100644 --- a/results/irm/lpq_metadata.csv +++ b/results/irm/lpq_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,LPQCoverageSimulation,2025-09-08 08:32,114.00595455567041,3.12.3,scripts/irm/lpq_config.yml +0.12.dev0,LPQCoverageSimulation,2025-12-04 19:04,114.70904120604197,3.12.3,scripts/irm/lpq_config.yml From b68a32f4becd06bc99d694ed9f8f93e5c56c097a Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:08:55 +0000 Subject: [PATCH 37/51] Update results from script: scripts/did/did_pa_multi.py --- results/did/did_pa_multi_detailed.csv | 96 ++++++++++++------------- results/did/did_pa_multi_eventstudy.csv | 96 ++++++++++++------------- results/did/did_pa_multi_group.csv | 96 ++++++++++++------------- results/did/did_pa_multi_metadata.csv | 2 +- results/did/did_pa_multi_time.csv | 96 ++++++++++++------------- 5 files changed, 193 insertions(+), 193 deletions(-) diff --git a/results/did/did_pa_multi_detailed.csv b/results/did/did_pa_multi_detailed.csv index 23642ea3..32a34122 100644 --- a/results/did/did_pa_multi_detailed.csv +++ b/results/did/did_pa_multi_detailed.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.7108333333333333,1.1436165638794173,0.4412630010586681,0.576,1.704191694806129,4.269417577492364,3.9140347262023947,,500 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.802,1.3627031856210492,0.4412630010586681,0.71,1.8930673922110166,4.269417577492364,3.9140347262023947,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.6508333333333334,1.0012390891898755,0.44951975947060363,0.446,1.5216831112331473,3.621048176660743,3.6545608858238317,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.739,1.1930499605383666,0.44951975947060363,0.582,1.6786175048159389,3.621048176660743,3.6545608858238317,,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.9,0.9103333333333333,1.003018592145389,0.23617184097950658,0.906,1.5231824028315617,3.6659724327745824,3.6687928901843088,,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.957,1.1951703690939004,0.23617184097950658,0.962,1.6836624489601968,3.6659724327745824,3.6687928901843088,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.9,0.7091666666666666,1.1432873931586964,0.4428580695598743,0.554,1.7034459189904412,4.263969908752096,3.91405763516209,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.95,0.8026666666666666,1.3623109545150063,0.4428580695598743,0.7,1.8898347679889276,4.263969908752096,3.91405763516209,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.9,0.6483333333333333,1.0011671815445324,0.44985582150145925,0.472,1.520874058829834,3.6195473607527537,3.652612223265004,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.95,0.7353333333333334,1.1929642772941091,0.44985582150145925,0.588,1.6794441763027343,3.6195473607527537,3.652612223265004,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.9,0.9083333333333333,1.003341605401925,0.23605498778636974,0.908,1.5236021402644524,3.6655617458978376,3.6732418395448203,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.95,0.955,1.1955552631288262,0.23605498778636974,0.964,1.6837341484448498,3.6655617458978376,3.6732418395448203,,500 -LGBM Regr.,LGBM Clas.,observational,False,1,0.9,0.9226666666666666,4.246392676014023,1.0704916062622525,0.966,6.578448038420458,4.268055195404975,3.9153614349044483,0.8577841543409016,500 -LGBM Regr.,LGBM Clas.,observational,False,1,0.95,0.9721666666666666,5.059888960835597,1.0704916062622525,0.992,7.226560052337621,4.268055195404975,3.9153614349044483,0.8577841543409016,500 -LGBM Regr.,LGBM Clas.,observational,False,4,0.9,0.935,5.24144240451079,1.3120900692699173,0.986,8.036865203237454,3.6210466042543774,3.651313791030827,0.8536080734313474,500 -LGBM Regr.,LGBM Clas.,observational,False,4,0.95,0.9803333333333334,6.2455638432229055,1.3120900692699173,0.994,8.853512099308475,3.6210466042543774,3.651313791030827,0.8536080734313474,500 -LGBM Regr.,LGBM Clas.,observational,False,6,0.9,0.9346666666666666,3.876908061735824,0.912896803261912,0.972,6.010491070716655,3.666795349245793,3.6684371854222233,0.8824458183410678,500 -LGBM Regr.,LGBM Clas.,observational,False,6,0.95,0.9765,4.61962088776145,0.912896803261912,0.986,6.607553358697693,3.666795349245793,3.6684371854222233,0.8824458183410678,500 -LGBM Regr.,LGBM Clas.,observational,True,1,0.9,0.9141666666666667,1.785263211281463,0.4377233949733758,0.934,2.776087591964191,4.264912994803874,3.91674943720863,0.858124578723283,500 -LGBM Regr.,LGBM Clas.,observational,True,1,0.95,0.9633333333333334,2.1272723236298163,0.4377233949733758,0.968,3.04881244695313,4.264912994803874,3.91674943720863,0.858124578723283,500 -LGBM Regr.,LGBM Clas.,observational,True,4,0.9,0.9183333333333333,2.161887891712943,0.5070193732964262,0.932,3.3314534536503886,3.6214703404808577,3.6516561199001734,0.8538647372576891,500 -LGBM Regr.,LGBM Clas.,observational,True,4,0.95,0.9655,2.5760483102826313,0.5070193732964262,0.956,3.669592891135713,3.6214703404808577,3.6516561199001734,0.8538647372576891,500 -LGBM Regr.,LGBM Clas.,observational,True,6,0.9,0.9118333333333334,1.7606890952900482,0.42075655325163114,0.944,2.736680829873217,3.6623282629507616,3.667380623747617,0.8821558756328344,500 -LGBM Regr.,LGBM Clas.,observational,True,6,0.95,0.9633333333333334,2.09799045835871,0.42075655325163114,0.978,3.006060409939102,3.6623282629507616,3.667380623747617,0.8821558756328344,500 -Linear,Logistic,experimental,False,1,0.9,0.8808333333333334,0.418866478279303,0.10877121678369137,0.834,0.6529470500891298,1.4305249692200885,1.432190456683633,,500 -Linear,Logistic,experimental,False,1,0.95,0.9331666666666666,0.4991101933368467,0.10877121678369137,0.906,0.7168101775846977,1.4305249692200885,1.432190456683633,,500 -Linear,Logistic,experimental,False,4,0.9,0.5753333333333334,1.3942432174121073,0.7197802482990261,0.356,2.0179217121712334,4.624958585767405,4.6817355343203015,,500 -Linear,Logistic,experimental,False,4,0.95,0.6698333333333334,1.6613432630364986,0.7197802482990261,0.482,2.2498108523526943,4.624958585767405,4.6817355343203015,,500 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+Linear,Logistic,observational,True,6,0.9,0.8926666666666666,1.6941211069824236,0.4296741461421564,0.906,2.120335772428661,4.789419332950871,4.818343116282321,0.6974028333059313,500 +Linear,Logistic,observational,True,6,0.95,0.954,2.018669807895703,0.4296741461421564,0.952,2.4159333102414347,4.789419332950871,4.818343116282321,0.6974028333059313,500 diff --git a/results/did/did_pa_multi_metadata.csv b/results/did/did_pa_multi_metadata.csv index c14ef0d6..a7f3e805 100644 --- a/results/did/did_pa_multi_metadata.csv +++ b/results/did/did_pa_multi_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDMultiCoverageSimulation,2025-12-03 18:27,12.992252508799234,3.12.9,scripts/did/did_pa_multi_config.yml +0.12.dev0,DIDMultiCoverageSimulation,2025-12-04 19:08,118.96503913799921,3.12.3,scripts/did/did_pa_multi_config.yml diff --git a/results/did/did_pa_multi_time.csv b/results/did/did_pa_multi_time.csv index 9949ef7d..543f6f21 100644 --- a/results/did/did_pa_multi_time.csv +++ b/results/did/did_pa_multi_time.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_control,Loss g_treated,Loss m,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.608,1.134693405137764,0.5006000229178051,0.58,1.3373266853018126,4.269417577492364,3.9140347262023947,,500 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.716,1.3520705861754752,0.5006000229178051,0.698,1.5469943781454005,4.269417577492364,3.9140347262023947,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.436,0.9487919156211294,0.5455703972080982,0.396,1.1368800976585398,3.621048176660743,3.6545608858238317,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.558,1.1305552986418062,0.5455703972080982,0.504,1.307106625962181,3.621048176660743,3.6545608858238317,,500 -LGBM Regr.,LGBM 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-Linear,Logistic,observational,False,1,0.9,0.8873333333333334,0.3842361494786204,0.09502707859290638,0.886,0.4927986549739525,1.430409587506778,1.4324863844056221,0.6762284233665801,500 -Linear,Logistic,observational,False,1,0.95,0.944,0.4578456114251332,0.09502707859290638,0.94,0.5558001935505136,1.430409587506778,1.4324863844056221,0.6762284233665801,500 -Linear,Logistic,observational,False,4,0.9,0.5346666666666666,1.8588178500844048,0.8564365743323401,0.508,2.1091189235370176,4.624523121930485,4.681651215529987,0.6756504408066538,500 -Linear,Logistic,observational,False,4,0.95,0.6506666666666666,2.214918081639791,0.8564365743323401,0.63,2.457644312114233,4.624523121930485,4.681651215529987,0.6756504408066538,500 -Linear,Logistic,observational,False,6,0.9,0.9086666666666666,1.5774820636844875,0.3911546821727288,0.906,1.8125445410478398,4.807036455087926,4.8147015452372015,0.6974024454123922,500 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-Linear,Logistic,observational,True,6,0.9,0.9033333333333333,1.5341853329208817,0.39061849014153127,0.906,1.7621100729507977,4.807530825853383,4.815937542018182,0.6974023190896237,500 -Linear,Logistic,observational,True,6,0.95,0.9473333333333334,1.8280945786692995,0.39061849014153127,0.94,2.047037540792628,4.807530825853383,4.815937542018182,0.6974023190896237,500 +LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.598,1.1266899230627856,0.5018460987334973,0.57,1.330071739758634,4.251414560174687,3.9023721235530875,,500 +LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.7186666666666667,1.3425338490696075,0.5018460987334973,0.69,1.5336397043833967,4.251414560174687,3.9023721235530875,,500 +LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.45,0.9446174454063001,0.531224200831128,0.41,1.132386874411178,3.6041257872434316,3.656177733062923,,500 +LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.5646666666666667,1.125581110579392,0.531224200831128,0.54,1.3045910589819267,3.6041257872434316,3.656177733062923,,500 +LGBM Regr.,LGBM Clas.,experimental,False,6,0.9,0.8786666666666666,0.9486213522201865,0.24397005084817594,0.872,1.1374661252630853,3.6653199008882806,3.6732792671089802,,500 +LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.9353333333333333,1.1303520598140762,0.24397005084817594,0.922,1.3078779640488225,3.6653199008882806,3.6732792671089802,,500 +LGBM Regr.,LGBM Clas.,experimental,True,1,0.9,0.602,1.1276256196412848,0.5027730970734264,0.558,1.3291592714915261,4.254915661245208,3.9081580572672006,,500 +LGBM Regr.,LGBM Clas.,experimental,True,1,0.95,0.71,1.3436488003116305,0.5027730970734264,0.68,1.5394934609066147,4.254915661245208,3.9081580572672006,,500 +LGBM Regr.,LGBM Clas.,experimental,True,4,0.9,0.448,0.9442163183851058,0.53162433456125,0.424,1.131334371992665,3.604058108148232,3.6558496288293028,,500 +LGBM Regr.,LGBM Clas.,experimental,True,4,0.95,0.5686666666666667,1.125103138252928,0.53162433456125,0.516,1.3002854383058795,3.604058108148232,3.6558496288293028,,500 +LGBM Regr.,LGBM Clas.,experimental,True,6,0.9,0.8806666666666666,0.9483497352597887,0.243361151862621,0.878,1.1361944209715906,3.6673476578091213,3.67282635262856,,500 +LGBM Regr.,LGBM Clas.,experimental,True,6,0.95,0.9393333333333334,1.1300284082433547,0.243361151862621,0.944,1.309480511069928,3.6673476578091213,3.67282635262856,,500 +LGBM Regr.,LGBM Clas.,observational,False,1,0.9,0.9453333333333334,4.318921692723004,1.0247135023367768,0.96,5.331034987139861,4.253175709801001,3.906060351285797,0.8593178740429351,500 +LGBM Regr.,LGBM Clas.,observational,False,1,0.95,0.982,5.146312614742826,1.0247135023367768,0.984,6.103722794129011,4.253175709801001,3.906060351285797,0.8593178740429351,500 +LGBM Regr.,LGBM Clas.,observational,False,4,0.9,0.9366666666666666,5.585581251950944,1.4080679871607733,0.97,6.815946663074795,3.603393726868437,3.6567299092669496,0.8550452901236911,500 +LGBM Regr.,LGBM Clas.,observational,False,4,0.95,0.9826666666666666,6.655630572329935,1.4080679871607733,0.99,7.817978753131027,3.603393726868437,3.6567299092669496,0.8550452901236911,500 +LGBM Regr.,LGBM Clas.,observational,False,6,0.9,0.9386666666666666,3.6293975429293828,0.8102130923310369,0.936,4.4986699843552875,3.6690522171173754,3.673958530682985,0.8827023979591501,500 +LGBM Regr.,LGBM Clas.,observational,False,6,0.95,0.97,4.324693913891721,0.8102130923310369,0.974,5.145052250164915,3.6690522171173754,3.673958530682985,0.8827023979591501,500 +LGBM Regr.,LGBM Clas.,observational,True,1,0.9,0.9446666666666667,1.7146248702392077,0.3845887599583407,0.952,2.122357161809936,4.252871492540774,3.9091800675023842,0.8597712039752193,500 +LGBM Regr.,LGBM Clas.,observational,True,1,0.95,0.9753333333333334,2.043101548734134,0.3845887599583407,0.984,2.4227835779991147,4.252871492540774,3.9091800675023842,0.8597712039752193,500 +LGBM Regr.,LGBM Clas.,observational,True,4,0.9,0.908,2.1781319403080635,0.499822909447953,0.904,2.662414622198057,3.6023625666216796,3.656369960958097,0.8544314031214001,500 +LGBM Regr.,LGBM Clas.,observational,True,4,0.95,0.956,2.5954042880352297,0.499822909447953,0.956,3.0495153098435317,3.6023625666216796,3.656369960958097,0.8544314031214001,500 +LGBM Regr.,LGBM Clas.,observational,True,6,0.9,0.9053333333333333,1.6207287453832842,0.3906756266619106,0.916,2.0075174634593704,3.6668860766842997,3.673219727253148,0.8823772245412226,500 +LGBM Regr.,LGBM Clas.,observational,True,6,0.95,0.9593333333333334,1.9312174151005732,0.3906756266619106,0.968,2.2937511551154683,3.6668860766842997,3.673219727253148,0.8823772245412226,500 +Linear,Logistic,experimental,False,1,0.9,0.8546666666666666,0.34758756945888863,0.09593511221834335,0.818,0.4448743317621195,1.4308932901376943,1.433755616875065,,500 +Linear,Logistic,experimental,False,1,0.95,0.912,0.41417613485515037,0.09593511221834335,0.904,0.5036158003678926,1.4308932901376943,1.433755616875065,,500 +Linear,Logistic,experimental,False,4,0.9,0.3073333333333333,1.3703723623901347,0.9281321270334562,0.284,1.5743939019776392,4.630168890637375,4.67631587765691,,500 +Linear,Logistic,experimental,False,4,0.95,0.4046666666666667,1.6328993848964386,0.9281321270334562,0.372,1.8272212980954847,4.630168890637375,4.67631587765691,,500 +Linear,Logistic,experimental,False,6,0.9,0.8973333333333333,1.3691849992126803,0.33438744989071734,0.882,1.5774961611220242,4.789675142322002,4.82008231089315,,500 +Linear,Logistic,experimental,False,6,0.95,0.9393333333333334,1.631484554405599,0.33438744989071734,0.944,1.8315759696444804,4.789675142322002,4.82008231089315,,500 +Linear,Logistic,experimental,True,1,0.9,0.854,0.3476256774638225,0.09592672413351071,0.82,0.44577210308958737,1.4308342472003583,1.4336678133715806,,500 +Linear,Logistic,experimental,True,1,0.95,0.9126666666666666,0.4142215433437656,0.09592672413351071,0.906,0.5034315093950756,1.4308342472003583,1.4336678133715806,,500 +Linear,Logistic,experimental,True,4,0.9,0.31,1.3705059852330193,0.9278020085931696,0.28,1.5754546728538281,4.6324226237971935,4.67531768790484,,500 +Linear,Logistic,experimental,True,4,0.95,0.404,1.6330586063343064,0.9278020085931696,0.378,1.8301846344355657,4.6324226237971935,4.67531768790484,,500 +Linear,Logistic,experimental,True,6,0.9,0.9006666666666666,1.3691323148952537,0.33362437049025256,0.902,1.5750623510647086,4.78576655831704,4.816427096646486,,500 +Linear,Logistic,experimental,True,6,0.95,0.94,1.6314217771693669,0.33362437049025256,0.94,1.8315600699985277,4.78576655831704,4.816427096646486,,500 +Linear,Logistic,observational,False,1,0.9,0.8946666666666666,0.3827009755100664,0.09548442860762694,0.878,0.4903343385546143,1.4307576389325865,1.43345005442208,0.6767021399480564,500 +Linear,Logistic,observational,False,1,0.95,0.9426666666666667,0.4560163388144475,0.09548442860762694,0.938,0.5544191957158142,1.4307576389325865,1.43345005442208,0.6767021399480564,500 +Linear,Logistic,observational,False,4,0.9,0.5286666666666666,1.839570456380273,0.8682427324889391,0.484,2.0881522185500683,4.632192822877654,4.674876689774085,0.6765506392804086,500 +Linear,Logistic,observational,False,4,0.95,0.6406666666666666,2.1919833974598504,0.8682427324889391,0.612,2.4350218067084883,4.632192822877654,4.674876689774085,0.6765506392804086,500 +Linear,Logistic,observational,False,6,0.9,0.8933333333333334,1.539608824860191,0.3909689185717702,0.894,1.7736294511077006,4.7881148275256455,4.815953043151829,0.6973883348636404,500 +Linear,Logistic,observational,False,6,0.95,0.9473333333333334,1.8345570679129113,0.3909689185717702,0.94,2.0584684662967594,4.7881148275256455,4.815953043151829,0.6973883348636404,500 +Linear,Logistic,observational,True,1,0.9,0.8886666666666666,0.37870477166944744,0.0955501582341831,0.866,0.4847724241254767,1.4307941803206001,1.434054951101132,0.6767148696162103,500 +Linear,Logistic,observational,True,1,0.95,0.9353333333333333,0.45125456823853904,0.0955501582341831,0.936,0.5482294644122102,1.4307941803206001,1.434054951101132,0.6767148696162103,500 +Linear,Logistic,observational,True,4,0.9,0.5206666666666666,1.8296827662295978,0.8696986777550378,0.484,2.0768019122880257,4.633353199848344,4.672360931794965,0.6765044995038704,500 +Linear,Logistic,observational,True,4,0.95,0.64,2.1802014879524787,0.8696986777550378,0.616,2.418714420244346,4.633353199848344,4.672360931794965,0.6765044995038704,500 +Linear,Logistic,observational,True,6,0.9,0.892,1.5223480106061822,0.38943694227197045,0.886,1.7571128386894448,4.789419332950871,4.818343116282321,0.6974028333059313,500 +Linear,Logistic,observational,True,6,0.95,0.9426666666666667,1.8139895391508574,0.38943694227197045,0.94,2.0373261346926905,4.789419332950871,4.818343116282321,0.6974028333059313,500 From 76660f34a5447ed2a900da924c572a884fbcd3dd Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:07:06 +0000 Subject: [PATCH 38/51] Update results from script: scripts/irm/pq.py --- results/irm/pq_Y0_coverage.csv | 16 ++++++++-------- results/irm/pq_Y1_coverage.csv | 16 ++++++++-------- results/irm/pq_effect_coverage.csv | 16 ++++++++-------- results/irm/pq_metadata.csv | 2 +- 4 files changed, 25 insertions(+), 25 deletions(-) diff --git a/results/irm/pq_Y0_coverage.csv b/results/irm/pq_Y0_coverage.csv index 35c0bf7c..0477339e 100644 --- a/results/irm/pq_Y0_coverage.csv +++ b/results/irm/pq_Y0_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Clas.,LGBM Clas.,0.9,0.875,0.5729628686924746,0.14933104662607077,200 -LGBM Clas.,LGBM Clas.,0.95,0.9435714285714286,0.6827273677824547,0.14933104662607077,200 -LGBM Clas.,Logistic,0.9,0.8485714285714286,0.4108296025589305,0.11673180804759976,200 -LGBM Clas.,Logistic,0.95,0.9157142857142857,0.4895336652482356,0.11673180804759976,200 -Logistic,LGBM Clas.,0.9,0.9135714285714286,0.5672228429384693,0.13384657876544737,200 -Logistic,LGBM Clas.,0.95,0.9542857142857143,0.6758877052350742,0.13384657876544737,200 -Logistic,Logistic,0.9,0.8807142857142857,0.4077258841626537,0.10598223062286814,200 -Logistic,Logistic,0.95,0.937142857142857,0.4858353566722128,0.10598223062286814,200 +LGBM Clas.,LGBM Clas.,0.9,0.8835714285714286,0.577281292284823,0.14271934514647347,200 +LGBM Clas.,LGBM Clas.,0.95,0.9392857142857143,0.6878730868739935,0.14271934514647347,200 +LGBM Clas.,Logistic,0.9,0.8607142857142857,0.40881666521282084,0.11483649396585474,200 +LGBM Clas.,Logistic,0.95,0.9142857142857143,0.4871351024601153,0.11483649396585474,200 +Logistic,LGBM Clas.,0.9,0.9042857142857144,0.5673797076514296,0.13296062006394363,200 +Logistic,LGBM Clas.,0.95,0.952857142857143,0.676074621069292,0.13296062006394363,200 +Logistic,Logistic,0.9,0.8942857142857144,0.40507948963962115,0.10265133090656293,200 +Logistic,Logistic,0.95,0.945,0.4826819831020422,0.10265133090656293,200 diff --git a/results/irm/pq_Y1_coverage.csv b/results/irm/pq_Y1_coverage.csv index 8165f331..1525a432 100644 --- a/results/irm/pq_Y1_coverage.csv +++ b/results/irm/pq_Y1_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Clas.,LGBM Clas.,0.9,0.9057142857142857,0.25162636495287866,0.058705977832479536,200 -LGBM Clas.,LGBM Clas.,0.95,0.9535714285714286,0.2998313070460974,0.058705977832479536,200 -LGBM Clas.,Logistic,0.9,0.8964285714285714,0.23573718513618783,0.05676150939840453,200 -LGBM Clas.,Logistic,0.95,0.9442857142857143,0.28089818152397245,0.05676150939840453,200 -Logistic,LGBM Clas.,0.9,0.8935714285714286,0.252774168924015,0.06085432107563776,200 -Logistic,LGBM Clas.,0.95,0.9585714285714286,0.3011989998352174,0.06085432107563776,200 -Logistic,Logistic,0.9,0.9035714285714286,0.23702882479533696,0.05586671900446994,200 -Logistic,Logistic,0.95,0.9542857142857143,0.2824372651065206,0.05586671900446994,200 +LGBM Clas.,LGBM Clas.,0.9,0.8992857142857144,0.25462792812846624,0.061574078936371575,200 +LGBM Clas.,LGBM Clas.,0.95,0.955,0.3034078901688016,0.061574078936371575,200 +LGBM Clas.,Logistic,0.9,0.9,0.23716922863199283,0.056662068851699325,200 +LGBM Clas.,Logistic,0.95,0.947857142857143,0.2826045665968343,0.056662068851699325,200 +Logistic,LGBM Clas.,0.9,0.91,0.25350757405611474,0.06057756591497565,200 +Logistic,LGBM Clas.,0.95,0.952857142857143,0.30207290595150593,0.06057756591497565,200 +Logistic,Logistic,0.9,0.9085714285714286,0.23739933997656035,0.05623615248006517,200 +Logistic,Logistic,0.95,0.9485714285714286,0.28287876117585126,0.05623615248006517,200 diff --git a/results/irm/pq_effect_coverage.csv b/results/irm/pq_effect_coverage.csv index 6e2eac79..4f445048 100644 --- a/results/irm/pq_effect_coverage.csv +++ b/results/irm/pq_effect_coverage.csv @@ -1,9 +1,9 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Clas.,LGBM Clas.,0.9,0.8735714285714287,0.6116337215076973,0.1558680043819949,0.87,0.8751495566106742,200 -LGBM Clas.,LGBM Clas.,0.95,0.9364285714285714,0.7288065310146017,0.1558680043819949,0.935,0.9742335603492159,200 -LGBM Clas.,Logistic,0.9,0.8657142857142857,0.4526161311109992,0.12306500536219656,0.84,0.649451495457938,200 -LGBM Clas.,Logistic,0.95,0.9228571428571429,0.5393253851064949,0.12306500536219656,0.895,0.7232469343557514,200 -Logistic,LGBM Clas.,0.9,0.9128571428571429,0.6095967182261998,0.14021240836441295,0.91,0.8594719336371053,200 -Logistic,LGBM Clas.,0.95,0.9607142857142857,0.7263792918957479,0.14021240836441295,0.955,0.9608239489842615,200 -Logistic,Logistic,0.9,0.8914285714285713,0.45472908142368906,0.11565410839324233,0.85,0.6443440528558397,200 -Logistic,Logistic,0.95,0.9364285714285714,0.5418431206947187,0.11565410839324233,0.935,0.7181545386323929,200 +LGBM Clas.,LGBM Clas.,0.9,0.8821428571428571,0.617101804869696,0.1564169206328714,0.815,0.883883838205828,200 +LGBM Clas.,LGBM Clas.,0.95,0.9342857142857143,0.7353221542155811,0.1564169206328714,0.88,0.9823650789937496,200 +LGBM Clas.,Logistic,0.9,0.8478571428571428,0.4518034235096248,0.12829648750837963,0.8,0.6470666470391672,200 +LGBM Clas.,Logistic,0.95,0.9235714285714286,0.5383569842697542,0.12829648750837963,0.865,0.7203742550212913,200 +Logistic,LGBM Clas.,0.9,0.8992857142857144,0.6101152858243912,0.14615613151121945,0.865,0.8609298901368315,200 +Logistic,LGBM Clas.,0.95,0.95,0.7269972033009643,0.14615613151121945,0.935,0.9616862862711715,200 +Logistic,Logistic,0.9,0.8707142857142857,0.45253064924826814,0.11963858185845028,0.85,0.6410713247109417,200 +Logistic,Logistic,0.95,0.9364285714285714,0.5392235271845868,0.11963858185845028,0.915,0.7157783672533653,200 diff --git a/results/irm/pq_metadata.csv b/results/irm/pq_metadata.csv index 401f7932..d892f303 100644 --- a/results/irm/pq_metadata.csv +++ b/results/irm/pq_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PQCoverageSimulation,2025-09-08 08:35,117.05562915007273,3.12.3,scripts/irm/pq_config.yml +0.12.dev0,PQCoverageSimulation,2025-12-04 19:07,117.31327378352483,3.12.3,scripts/irm/pq_config.yml From 25628e7639edbd585ae6805b2f22b3a6acc061d5 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:17:16 +0000 Subject: [PATCH 39/51] Update results from script: scripts/irm/irm_atte.py --- results/irm/irm_atte_coverage.csv | 28 ++++++++++++++-------------- results/irm/irm_atte_metadata.csv | 2 +- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/results/irm/irm_atte_coverage.csv b/results/irm/irm_atte_coverage.csv index d5232ffa..bdb6bc26 100644 --- a/results/irm/irm_atte_coverage.csv +++ b/results/irm/irm_atte_coverage.csv @@ -1,15 +1,15 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,0.929,1.4473786736685832,0.3373613664530304,1000 -LGBM Regr.,LGBM Clas.,0.95,0.979,1.7246580643406204,0.3373613664530304,1000 -LGBM Regr.,Logistic,0.9,0.889,0.8389047954622636,0.21725546577056573,1000 -LGBM Regr.,Logistic,0.95,0.951,0.9996167188513524,0.21725546577056573,1000 -LassoCV,LGBM Clas.,0.9,0.915,1.3683067740446384,0.33736290758652415,1000 -LassoCV,LGBM Clas.,0.95,0.961,1.6304380845729798,0.33736290758652415,1000 -LassoCV,Logistic,0.9,0.889,0.7878378087583325,0.20635919443627296,1000 -LassoCV,Logistic,0.95,0.949,0.9387666510406417,0.20635919443627296,1000 -LassoCV,RF Clas.,0.9,0.898,0.577413813407202,0.14935353360568673,1000 -LassoCV,RF Clas.,0.95,0.946,0.6880309955309085,0.14935353360568673,1000 -RF Regr.,Logistic,0.9,0.886,0.8121231811390474,0.21168816957084619,1000 -RF Regr.,Logistic,0.95,0.951,0.9677044570784729,0.21168816957084619,1000 -RF Regr.,RF Clas.,0.9,0.882,0.5936731847885253,0.154203377470952,1000 -RF Regr.,RF Clas.,0.95,0.937,0.7074052315094118,0.154203377470952,1000 +LGBM Regr.,LGBM Clas.,0.9,0.929,1.5129808992585232,0.3494762511606464,1000 +LGBM Regr.,LGBM Clas.,0.95,0.975,1.802827937547063,0.3494762511606464,1000 +LGBM Regr.,Logistic,0.9,0.897,0.8665295499675973,0.21830700047216328,1000 +LGBM Regr.,Logistic,0.95,0.946,1.0325336441175617,0.21830700047216328,1000 +LassoCV,LGBM Clas.,0.9,0.916,1.3734552128644464,0.3386570485257962,1000 +LassoCV,LGBM Clas.,0.95,0.974,1.6365728278097587,0.3386570485257962,1000 +LassoCV,Logistic,0.9,0.903,0.8185274172270134,0.2143949995500193,1000 +LassoCV,Logistic,0.95,0.96,0.9753355750547081,0.2143949995500193,1000 +LassoCV,RF Clas.,0.9,0.884,0.5768241077018054,0.15075762219708613,1000 +LassoCV,RF Clas.,0.95,0.944,0.6873283178426831,0.15075762219708613,1000 +RF Regr.,Logistic,0.9,0.896,0.8425822825234105,0.2141783904054886,1000 +RF Regr.,Logistic,0.95,0.94,1.0039987149605252,0.2141783904054886,1000 +RF Regr.,RF Clas.,0.9,0.873,0.5972373860549938,0.1572551056347038,1000 +RF Regr.,RF Clas.,0.95,0.935,0.7116522392683193,0.1572551056347038,1000 diff --git a/results/irm/irm_atte_metadata.csv b/results/irm/irm_atte_metadata.csv index 6cb63ab1..97b86a9f 100644 --- a/results/irm/irm_atte_metadata.csv +++ b/results/irm/irm_atte_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMATTECoverageSimulation,2025-09-08 08:43,124.6468609491984,3.12.3,scripts/irm/irm_atte_config.yml +0.12.dev0,IRMATTECoverageSimulation,2025-12-04 19:17,127.50641198952992,3.12.3,scripts/irm/irm_atte_config.yml From 1b918afcbfb81aa20662c329060269264ec964f7 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:18:09 +0000 Subject: [PATCH 40/51] Update results from script: scripts/irm/irm_ate.py --- results/irm/irm_ate_coverage.csv | 28 ++++++++++++++-------------- results/irm/irm_ate_metadata.csv | 2 +- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/results/irm/irm_ate_coverage.csv b/results/irm/irm_ate_coverage.csv index 803a7f1f..6c991106 100644 --- a/results/irm/irm_ate_coverage.csv +++ b/results/irm/irm_ate_coverage.csv @@ -1,15 +1,15 @@ Learner g,Learner m,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,0.9,0.931,1.2080355246306695,0.2860783963960894,1000 -LGBM Regr.,LGBM Clas.,0.95,0.978,1.4394631118084988,0.2860783963960894,1000 -LGBM Regr.,Logistic,0.9,0.913,0.7728307106222837,0.19455600487602173,1000 -LGBM Regr.,Logistic,0.95,0.97,0.920884590669332,0.19455600487602173,1000 -LassoCV,LGBM Clas.,0.9,0.92,1.1003887936231362,0.25860854058964594,1000 -LassoCV,LGBM Clas.,0.95,0.972,1.3111941203485913,0.25860854058964594,1000 -LassoCV,Logistic,0.9,0.9,0.6549601688327414,0.1653729067630603,1000 -LassoCV,Logistic,0.95,0.95,0.7804331772667328,0.1653729067630603,1000 -LassoCV,RF Clas.,0.9,0.903,0.6018263796986386,0.14720572978308205,1000 -LassoCV,RF Clas.,0.95,0.949,0.7171203624614432,0.14720572978308205,1000 -RF Regr.,Logistic,0.9,0.911,0.733375696800298,0.1843476916310545,1000 -RF Regr.,Logistic,0.95,0.955,0.8738710419659472,0.1843476916310545,1000 -RF Regr.,RF Clas.,0.9,0.885,0.6190925784677545,0.1539369305670702,1000 -RF Regr.,RF Clas.,0.95,0.944,0.7376943072689804,0.1539369305670702,1000 +LGBM Regr.,LGBM Clas.,0.9,0.931,1.2316532622529917,0.2992448325629738,1000 +LGBM Regr.,LGBM Clas.,0.95,0.971,1.4676053819640873,0.2992448325629738,1000 +LGBM Regr.,Logistic,0.9,0.918,0.7686543138837709,0.19130776690526882,1000 +LGBM Regr.,Logistic,0.95,0.964,0.9159081070123597,0.19130776690526882,1000 +LassoCV,LGBM Clas.,0.9,0.919,1.085643937942505,0.26276932948403875,1000 +LassoCV,LGBM Clas.,0.95,0.977,1.2936245411363425,0.26276932948403875,1000 +LassoCV,Logistic,0.9,0.914,0.6575993935743172,0.15918300885857045,1000 +LassoCV,Logistic,0.95,0.963,0.7835780075153564,0.15918300885857045,1000 +LassoCV,RF Clas.,0.9,0.923,0.5880692523443405,0.14216834117620963,1000 +LassoCV,RF Clas.,0.95,0.968,0.7007277341428199,0.14216834117620963,1000 +RF Regr.,Logistic,0.9,0.912,0.737241521175503,0.18258213488785877,1000 +RF Regr.,Logistic,0.95,0.96,0.8784774558266153,0.18258213488785877,1000 +RF Regr.,RF Clas.,0.9,0.901,0.6169132543509753,0.15312602292680585,1000 +RF Regr.,RF Clas.,0.95,0.952,0.7350974824150611,0.15312602292680585,1000 diff --git a/results/irm/irm_ate_metadata.csv b/results/irm/irm_ate_metadata.csv index 38401a13..ba15362e 100644 --- a/results/irm/irm_ate_metadata.csv +++ b/results/irm/irm_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,IRMATECoverageSimulation,2025-09-08 08:44,125.62044833103816,3.12.3,scripts/irm/irm_ate_config.yml +0.12.dev0,IRMATECoverageSimulation,2025-12-04 19:18,128.43919472694398,3.12.3,scripts/irm/irm_ate_config.yml From 49b0ffd49bb13b394d82a614e4931376c33e21eb Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:28:28 +0000 Subject: [PATCH 41/51] Update results from script: scripts/plm/lplr_ate.py --- results/plm/lplr_ate_coverage.csv | 24 ++++++++++++------------ results/plm/lplr_ate_metadata.csv | 2 +- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/results/plm/lplr_ate_coverage.csv b/results/plm/lplr_ate_coverage.csv index 17f7507b..64da39ca 100644 --- a/results/plm/lplr_ate_coverage.csv +++ b/results/plm/lplr_ate_coverage.csv @@ -1,13 +1,13 @@ Learner m,Learner M,Learner t,Score,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,0.866,0.6573798859045776,0.17600558265832575,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,0.936,0.7833164479942107,0.17600558265832575,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,0.89,0.5881153537384244,0.15332249272864673,500 -LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,0.936,0.700782667342079,0.15332249272864673,500 -LassoCV,Logistic,LassoCV,instrument,0.9,0.858,0.5897233516083383,0.16268441455635813,500 -LassoCV,Logistic,LassoCV,instrument,0.95,0.916,0.7026987149834061,0.16268441455635813,500 -LassoCV,Logistic,LassoCV,nuisance_space,0.9,0.8937875751503006,0.576947311075238,0.1492081384708213,499 -LassoCV,Logistic,LassoCV,nuisance_space,0.95,0.9278557114228457,0.6874751237169234,0.1492081384708213,499 -RF Regr.,RF Clas.,RF Regr.,instrument,0.9,0.902,0.39485055228075816,0.09886061010323771,500 -RF Regr.,RF Clas.,RF Regr.,instrument,0.95,0.942,0.4704934524662526,0.09886061010323771,500 -RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.9,0.892,0.38461199091029774,0.09604302638290617,500 -RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.95,0.942,0.4582934541133308,0.09604302638290617,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.9,0.9,0.6534565055513932,0.16336115980855923,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,instrument,0.95,0.956,0.7786414519557212,0.16336115980855923,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.9,0.9,0.587173331700569,0.14523265588039966,500 +LGBM Regr.,LGBM Clas.,LGBM Regr.,nuisance_space,0.95,0.948,0.6996601788503451,0.14523265588039966,500 +LassoCV,Logistic,LassoCV,instrument,0.9,0.85,0.5909860504305657,0.16604923608569636,500 +LassoCV,Logistic,LassoCV,instrument,0.95,0.92,0.7042033134317637,0.16604923608569636,500 +LassoCV,Logistic,LassoCV,nuisance_space,0.9,0.872,0.5743427245911767,0.1561191992391632,500 +LassoCV,Logistic,LassoCV,nuisance_space,0.95,0.934,0.6843715674978567,0.1561191992391632,500 +RF Regr.,RF Clas.,RF Regr.,instrument,0.9,0.896,0.3948609235155087,0.09705489730756603,500 +RF Regr.,RF Clas.,RF Regr.,instrument,0.95,0.95,0.4705058105546887,0.09705489730756603,500 +RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.9,0.892,0.38159441513135645,0.09629310976419707,500 +RF Regr.,RF Clas.,RF Regr.,nuisance_space,0.95,0.936,0.4546977907968892,0.09629310976419707,500 diff --git a/results/plm/lplr_ate_metadata.csv b/results/plm/lplr_ate_metadata.csv index bc94ba00..5ceb9bd0 100644 --- a/results/plm/lplr_ate_metadata.csv +++ b/results/plm/lplr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,LPLRATECoverageSimulation,2025-11-26 13:24,14.800051196416218,3.12.9,scripts/plm/lplr_ate_config.yml +0.12.dev0,LPLRATECoverageSimulation,2025-12-04 19:28,138.79539552927017,3.12.3,scripts/plm/lplr_ate_config.yml From 6a6a4e8c23c75a18852e38676903e7068c3a8c51 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 19:46:13 +0000 Subject: [PATCH 42/51] Update results from script: scripts/ssm/ssm_nonig_ate.py --- results/ssm/ssm_nonig_ate_coverage.csv | 36 +++++++++++++------------- results/ssm/ssm_nonig_ate_metadata.csv | 2 +- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/results/ssm/ssm_nonig_ate_coverage.csv b/results/ssm/ssm_nonig_ate_coverage.csv index 17393dbd..c65e7897 100644 --- a/results/ssm/ssm_nonig_ate_coverage.csv +++ b/results/ssm/ssm_nonig_ate_coverage.csv @@ -1,19 +1,19 @@ Learner g,Learner m,Learner pi,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.903,1.481059324827102,0.37602754989426823,1000 -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.949,1.7647910355454204,0.37602754989426823,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.9,0.921,2.3407463966423263,0.6320506843624508,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.95,0.97,2.789171364058536,0.6320506843624508,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.9,0.801,1.0973850715933846,0.30960808498975473,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.95,0.888,1.307614964792486,0.30960808498975473,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.901,1.4523800778983884,0.3790291564092631,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.951,1.7306176050571476,0.3790291564092631,1000 -LassoCV,Logistic,Logistic,0.9,0.86,1.665667090195805,0.4740292503841448,1000 -LassoCV,Logistic,Logistic,0.95,0.919,1.9847647556749581,0.4740292503841448,1000 -LassoCV,RF Clas.,RF Clas.,0.9,0.767,0.6632288612919495,0.20996104153398967,1000 -LassoCV,RF Clas.,RF Clas.,0.95,0.846,0.7902859320369685,0.20996104153398967,1000 -RF Regr.,Logistic,RF Clas.,0.9,0.702,0.7343396895666414,0.2636200794468997,1000 -RF Regr.,Logistic,RF Clas.,0.95,0.805,0.8750197101954067,0.2636200794468997,1000 -RF Regr.,RF Clas.,Logistic,0.9,0.9,1.3848131749906887,0.3741841220064777,1000 -RF Regr.,RF Clas.,Logistic,0.95,0.964,1.6501066744332193,0.3741841220064777,1000 -RF Regr.,RF Clas.,RF Clas.,0.9,0.759,0.6767002671710868,0.21313672406339423,1000 -RF Regr.,RF Clas.,RF Clas.,0.95,0.838,0.8063381022189231,0.21313672406339423,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.894,1.5178763749801094,0.38450773557760864,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.953,1.8086612566608633,0.38450773557760864,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.9,0.927,2.0272591738127477,0.5256961421194926,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.95,0.967,2.4156282984070265,0.5256961421194926,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.9,0.817,1.1198920754331765,0.3135532959105554,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.95,0.897,1.3344337140131413,0.3135532959105554,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.875,1.4741185507565766,0.38203646823601195,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.947,1.7565205931302663,0.38203646823601195,1000 +LassoCV,Logistic,Logistic,0.9,0.867,1.7433117258806798,0.47873193386127033,1000 +LassoCV,Logistic,Logistic,0.95,0.92,2.077284045562859,0.47873193386127033,1000 +LassoCV,RF Clas.,RF Clas.,0.9,0.771,0.6586361648599308,0.20438857392516385,1000 +LassoCV,RF Clas.,RF Clas.,0.95,0.851,0.7848133966993618,0.20438857392516385,1000 +RF Regr.,Logistic,RF Clas.,0.9,0.727,0.7431109071370045,0.2591017476304121,1000 +RF Regr.,Logistic,RF Clas.,0.95,0.823,0.8854712605685171,0.2591017476304121,1000 +RF Regr.,RF Clas.,Logistic,0.9,0.905,1.4934904960565314,0.39775667461813907,1000 +RF Regr.,RF Clas.,Logistic,0.95,0.953,1.779603689690512,0.39775667461813907,1000 +RF Regr.,RF Clas.,RF Clas.,0.9,0.757,0.64946265840171,0.21125350705787166,1000 +RF Regr.,RF Clas.,RF Clas.,0.95,0.829,0.7738824895502671,0.21125350705787166,1000 diff --git a/results/ssm/ssm_nonig_ate_metadata.csv b/results/ssm/ssm_nonig_ate_metadata.csv index 2836c820..c7698960 100644 --- a/results/ssm/ssm_nonig_ate_metadata.csv +++ b/results/ssm/ssm_nonig_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,SSMNonIgnorableATECoverageSimulation,2025-09-08 09:10,151.7042101462682,3.12.3,scripts/ssm/ssm_nonig_ate_config.yml +0.12.dev0,SSMNonIgnorableATECoverageSimulation,2025-12-04 19:46,156.4951787908872,3.12.3,scripts/ssm/ssm_nonig_ate_config.yml From e5c1a052fa3e2224e22310480ecc4e5274035d1c Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 20:14:27 +0000 Subject: [PATCH 43/51] Update results from script: scripts/plm/plr_gate.py --- results/plm/plr_gate_coverage.csv | 56 +++++++++++++++---------------- results/plm/plr_gate_metadata.csv | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/results/plm/plr_gate_coverage.csv b/results/plm/plr_gate_coverage.csv index a32ab6dd..3e2dc9d7 100644 --- a/results/plm/plr_gate_coverage.csv +++ b/results/plm/plr_gate_coverage.csv @@ -1,29 +1,29 @@ Learner g,Learner m,Score,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Regr.,IV-type,0.9,0.8053333333333333,0.3409114324357686,0.10830011848130718,0.991,0.8017592987199822,1000 -LGBM Regr.,LGBM Regr.,IV-type,0.95,0.875,0.4062210269314008,0.10830011848130718,0.988,0.799796307936306,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.9,0.7383333333333334,0.4124415095407824,0.1366399549532388,0.984,0.9697388963048222,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.95,0.8283333333333334,0.49145437088373556,0.1366399549532388,0.984,0.9665687910235732,1000 -LGBM Regr.,LassoCV,IV-type,0.9,0.8936666666666666,0.35821185324988614,0.08767343268627296,0.999,0.8442099807695619,1000 -LGBM Regr.,LassoCV,IV-type,0.95,0.9493333333333334,0.4268357498206965,0.08767343268627296,0.999,0.8418490502463957,1000 -LGBM Regr.,LassoCV,partialling out,0.9,0.8396666666666667,0.5553838841615836,0.15655208513456628,0.995,1.3020013862133748,1000 -LGBM Regr.,LassoCV,partialling out,0.95,0.9026666666666666,0.6617807157516657,0.15655208513456628,0.996,1.3068899330904447,1000 -LassoCV,LGBM Regr.,IV-type,0.9,0.7393333333333334,0.35377029877119814,0.1253820270670297,0.986,0.8278870895082657,1000 -LassoCV,LGBM Regr.,IV-type,0.95,0.8236666666666667,0.42154331122861627,0.1253820270670297,0.983,0.8318320743030287,1000 -LassoCV,LGBM Regr.,partialling out,0.9,0.144,0.48091195717862995,0.4806335238545862,0.154,1.1322758027985098,1000 -LassoCV,LGBM Regr.,partialling out,0.95,0.191,0.5730419414593854,0.4806335238545862,0.156,1.1291510461071579,1000 -LassoCV,LassoCV,IV-type,0.9,0.9043333333333333,0.3571755626965762,0.0846962632601286,1.0,0.8373681252041506,1000 -LassoCV,LassoCV,IV-type,0.95,0.951,0.4256009334645622,0.0846962632601286,0.999,0.842347593014819,1000 -LassoCV,LassoCV,partialling out,0.9,0.8886666666666666,0.36826827935260703,0.08969215106833642,0.997,0.8647058876249634,1000 -LassoCV,LassoCV,partialling out,0.95,0.944,0.43881872061612937,0.08969215106833642,0.997,0.8654820471522434,1000 -LassoCV,RF Regr.,IV-type,0.9,0.8986666666666666,0.35592501324799986,0.08510323311217904,0.999,0.8366218670463145,1000 -LassoCV,RF Regr.,IV-type,0.95,0.9483333333333334,0.42411081188782424,0.08510323311217904,0.999,0.8366949606336201,1000 -LassoCV,RF Regr.,partialling out,0.9,0.7383333333333334,0.4030595445326118,0.13187581900072584,0.99,0.9493412021611323,1000 -LassoCV,RF Regr.,partialling out,0.95,0.833,0.48027507005177655,0.13187581900072584,0.991,0.9490391291628868,1000 -RF Regr.,LassoCV,IV-type,0.9,0.8883333333333334,0.3469399138393356,0.08520954380177768,1.0,0.8161804056188466,1000 -RF Regr.,LassoCV,IV-type,0.95,0.9433333333333334,0.413404405585196,0.08520954380177768,0.998,0.8113422076955483,1000 -RF Regr.,LassoCV,partialling out,0.9,0.867,0.41304588013208265,0.10868702662568211,0.998,0.9703778734057933,1000 -RF Regr.,LassoCV,partialling out,0.95,0.926,0.4921745228613064,0.10868702662568211,0.998,0.9699783780272438,1000 -RF Regr.,RF Regr.,IV-type,0.9,0.8883333333333334,0.34434350122208923,0.08569474479321623,0.998,0.808725225751662,1000 -RF Regr.,RF Regr.,IV-type,0.95,0.9463333333333334,0.410310589129175,0.08569474479321623,0.997,0.8079739610497064,1000 -RF Regr.,RF Regr.,partialling out,0.9,0.8866666666666666,0.36881414918104743,0.09303338687448989,0.999,0.8697573903122439,1000 -RF Regr.,RF Regr.,partialling out,0.95,0.9396666666666667,0.4394691646352566,0.09303338687448989,0.998,0.8652094967951268,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.9,0.806,0.340589476099536,0.10642719318016228,0.987,0.8004940282247246,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.95,0.876,0.4058373922946947,0.10642719318016228,0.983,0.8008650552894343,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.9,0.744,0.41216781442446043,0.13930890748776442,0.978,0.9711592306701852,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.95,0.8313333333333334,0.49112824303749686,0.13930890748776442,0.978,0.9674597871517567,1000 +LGBM Regr.,LassoCV,IV-type,0.9,0.8916666666666666,0.3577177065925385,0.09023588445552218,1.0,0.8406624541397631,1000 +LGBM Regr.,LassoCV,IV-type,0.95,0.9376666666666666,0.4262469377613054,0.09023588445552218,0.998,0.8396811667915306,1000 +LGBM Regr.,LassoCV,partialling out,0.9,0.851,0.5544324938243242,0.15307433721758,0.998,1.3050834424316233,1000 +LGBM Regr.,LassoCV,partialling out,0.95,0.9143333333333333,0.660647064242672,0.15307433721758,0.996,1.3090104136830218,1000 +LassoCV,LGBM Regr.,IV-type,0.9,0.749,0.35340599486147595,0.12225964634750337,0.977,0.8310427452560518,1000 +LassoCV,LGBM Regr.,IV-type,0.95,0.8353333333333334,0.421109216345775,0.12225964634750337,0.981,0.8273196308576153,1000 +LassoCV,LGBM Regr.,partialling out,0.9,0.13166666666666665,0.4816129404694944,0.48357343001492625,0.178,1.134778768017635,1000 +LassoCV,LGBM Regr.,partialling out,0.95,0.17466666666666666,0.5738772145690085,0.48357343001492625,0.171,1.1277997900457917,1000 +LassoCV,LassoCV,IV-type,0.9,0.8946666666666666,0.35674724983816697,0.0860954784479901,0.997,0.8404382654949778,1000 +LassoCV,LassoCV,IV-type,0.95,0.949,0.4250905672150416,0.0860954784479901,0.998,0.8392315655701177,1000 +LassoCV,LassoCV,partialling out,0.9,0.8956666666666666,0.36721873313399944,0.08935733034120422,1.0,0.8631111662911434,1000 +LassoCV,LassoCV,partialling out,0.95,0.9426666666666667,0.43756810916057176,0.08935733034120422,0.999,0.8663882678503069,1000 +LassoCV,RF Regr.,IV-type,0.9,0.8886666666666666,0.35548185477146965,0.08641145515163902,0.998,0.8365905528843341,1000 +LassoCV,RF Regr.,IV-type,0.95,0.9456666666666667,0.42358275599323825,0.08641145515163902,0.996,0.8364714904166475,1000 +LassoCV,RF Regr.,partialling out,0.9,0.7493333333333334,0.4049270401478124,0.13355260607357494,0.986,0.9506532809092213,1000 +LassoCV,RF Regr.,partialling out,0.95,0.8326666666666667,0.48250032832832196,0.13355260607357494,0.986,0.953296010770798,1000 +RF Regr.,LassoCV,IV-type,0.9,0.8846666666666666,0.3463895774754974,0.08804499016875705,0.996,0.8129149828061188,1000 +RF Regr.,LassoCV,IV-type,0.95,0.937,0.4127486393608757,0.08804499016875705,0.994,0.8138385193812449,1000 +RF Regr.,LassoCV,partialling out,0.9,0.8646666666666666,0.4124715835614797,0.1086839758361102,0.996,0.9727843917949384,1000 +RF Regr.,LassoCV,partialling out,0.95,0.9253333333333333,0.49149020628967766,0.1086839758361102,0.999,0.9742832383410707,1000 +RF Regr.,RF Regr.,IV-type,0.9,0.8823333333333334,0.3442575451597504,0.08764869687656554,0.998,0.8096053046000788,1000 +RF Regr.,RF Regr.,IV-type,0.95,0.9413333333333334,0.4102081661635831,0.08764869687656554,0.997,0.8116011089606914,1000 +RF Regr.,RF Regr.,partialling out,0.9,0.8836666666666666,0.3684983412988984,0.09321084514903152,0.997,0.8668637316494815,1000 +RF Regr.,RF Regr.,partialling out,0.95,0.9376666666666666,0.4390928563334698,0.09321084514903152,0.996,0.8668288739442291,1000 diff --git a/results/plm/plr_gate_metadata.csv b/results/plm/plr_gate_metadata.csv index 44ab86c4..10ef829b 100644 --- a/results/plm/plr_gate_metadata.csv +++ b/results/plm/plr_gate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLRGATECoverageSimulation,2025-11-17 11:46,184.07251759767533,3.12.3,scripts/plm/plr_gate_config.yml +0.12.dev0,PLRGATECoverageSimulation,2025-12-04 20:14,184.69524136781692,3.12.3,scripts/plm/plr_gate_config.yml From cc66b6c74f33330812500874ed1a58fad91860b0 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 20:18:47 +0000 Subject: [PATCH 44/51] Update results from script: scripts/plm/plr_cate.py --- results/plm/plr_cate_coverage.csv | 56 +++++++++++++++---------------- results/plm/plr_cate_metadata.csv | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/results/plm/plr_cate_coverage.csv b/results/plm/plr_cate_coverage.csv index c7c635f5..0e0f84a6 100644 --- a/results/plm/plr_cate_coverage.csv +++ b/results/plm/plr_cate_coverage.csv @@ -1,29 +1,29 @@ Learner g,Learner m,Score,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition -LGBM Regr.,LGBM Regr.,IV-type,0.9,0.82905,0.34859934457061187,0.10269066968736434,0.986,0.8788694707117327,1000 -LGBM Regr.,LGBM Regr.,IV-type,0.95,0.89328,0.41538173926087907,0.10269066968736434,0.986,0.8765365613685849,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.9,0.7487699999999999,0.4562033822396419,0.15530020302736622,0.973,1.1461116868127335,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.95,0.8297899999999999,0.5435998584702256,0.15530020302736622,0.971,1.1476869846192674,1000 -LGBM Regr.,LassoCV,IV-type,0.9,0.88228,0.3665735738309043,0.09237124180359486,0.998,0.9203983723665325,1000 -LGBM Regr.,LassoCV,IV-type,0.95,0.93635,0.436799354435143,0.09237124180359486,0.997,0.9195555991848416,1000 -LGBM Regr.,LassoCV,partialling out,0.9,0.84415,0.6446703702300751,0.18063600645569744,0.993,1.6102346810221977,1000 -LGBM Regr.,LassoCV,partialling out,0.95,0.9068200000000001,0.7681721259859722,0.18063600645569744,0.991,1.6197708088395464,1000 -LassoCV,LGBM Regr.,IV-type,0.9,0.78462,0.35820003669569267,0.11367456664699994,0.977,0.9022806080230871,1000 -LassoCV,LGBM Regr.,IV-type,0.95,0.86042,0.42682166952792083,0.11367456664699994,0.983,0.9028292164077011,1000 -LassoCV,LGBM Regr.,partialling out,0.9,0.11332,0.5635001570275089,0.5253287834841598,0.257,1.4151506337590742,1000 -LassoCV,LGBM Regr.,partialling out,0.95,0.16911,0.6714518513744726,0.5253287834841598,0.246,1.4217510537602984,1000 -LassoCV,LassoCV,IV-type,0.9,0.89467,0.3638287468934425,0.088964144313276,0.999,0.9149966883358348,1000 -LassoCV,LassoCV,IV-type,0.95,0.9472999999999999,0.4335286914089192,0.088964144313276,0.998,0.9154935052029363,1000 -LassoCV,LassoCV,partialling out,0.9,0.88946,0.3783305434334964,0.0937660325206101,0.998,0.9527467197251451,1000 -LassoCV,LassoCV,partialling out,0.95,0.94586,0.4508086477916106,0.0937660325206101,0.999,0.9523664378421313,1000 -LassoCV,RF Regr.,IV-type,0.9,0.89402,0.36133940754837507,0.08742978566108492,0.998,0.9102557308429199,1000 -LassoCV,RF Regr.,IV-type,0.95,0.9471499999999999,0.4305624606260176,0.08742978566108492,0.997,0.9103412832643125,1000 -LassoCV,RF Regr.,partialling out,0.9,0.76742,0.4333802817413689,0.14145970305557048,0.989,1.0911406471440919,1000 -LassoCV,RF Regr.,partialling out,0.95,0.8513,0.5164044568495605,0.14145970305557048,0.984,1.090270069893263,1000 -RF Regr.,LassoCV,IV-type,0.9,0.8809199999999999,0.3488601215296179,0.08868809382454168,0.996,0.8712649919759498,1000 -RF Regr.,LassoCV,IV-type,0.95,0.9355399999999999,0.41569247417325955,0.08868809382454168,0.998,0.87648182402068,1000 -RF Regr.,LassoCV,partialling out,0.9,0.8620399999999999,0.4453600356514868,0.11971854257293388,0.993,1.1194369351513875,1000 -RF Regr.,LassoCV,partialling out,0.95,0.92079,0.5306792140819111,0.11971854257293388,0.994,1.1126580820916288,1000 -RF Regr.,RF Regr.,IV-type,0.9,0.8773500000000001,0.34471572299390735,0.08798679363788052,0.996,0.8651769659880062,1000 -RF Regr.,RF Regr.,IV-type,0.95,0.93284,0.41075411872662454,0.08798679363788052,0.996,0.8664637796214006,1000 -RF Regr.,RF Regr.,partialling out,0.9,0.8784500000000001,0.3846648443860267,0.09849944709058014,0.996,0.9691346097579039,1000 -RF Regr.,RF Regr.,partialling out,0.95,0.93471,0.45835643291411227,0.09849944709058014,0.999,0.9675297074982309,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.9,0.81108,0.34753625073182437,0.10476467882955193,0.982,0.8753490918585222,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.95,0.88301,0.4141149848201987,0.10476467882955193,0.979,0.8696275590057301,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.9,0.7499,0.45436580938530496,0.15479317056430109,0.966,1.14045419057626,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.95,0.8292,0.5414102553624136,0.15479317056430109,0.965,1.1428606023655155,1000 +LGBM Regr.,LassoCV,IV-type,0.9,0.88042,0.36547250073661136,0.09244066038082599,0.995,0.9201052947485936,1000 +LGBM Regr.,LassoCV,IV-type,0.95,0.9352,0.435487344920254,0.09244066038082599,0.997,0.919191600876896,1000 +LGBM Regr.,LassoCV,partialling out,0.9,0.8518,0.6456523117846842,0.1769434790790047,0.996,1.624300550180789,1000 +LGBM Regr.,LassoCV,partialling out,0.95,0.9141,0.769342181515791,0.1769434790790047,0.998,1.6222295727135105,1000 +LassoCV,LGBM Regr.,IV-type,0.9,0.77595,0.3560003472875054,0.11493249002243157,0.98,0.8936995010150599,1000 +LassoCV,LGBM Regr.,IV-type,0.95,0.85296,0.42420057793254784,0.11493249002243157,0.979,0.8971381008472117,1000 +LassoCV,LGBM Regr.,partialling out,0.9,0.11575,0.5627449979830486,0.5297395114934405,0.226,1.412353729446771,1000 +LassoCV,LGBM Regr.,partialling out,0.95,0.17424,0.6705520238728092,0.5297395114934405,0.251,1.4195918591195151,1000 +LassoCV,LassoCV,IV-type,0.9,0.89501,0.3616476276526723,0.0887937798304664,0.996,0.9109478256370118,1000 +LassoCV,LassoCV,IV-type,0.95,0.94635,0.4309297275328325,0.0887937798304664,0.997,0.9076084670232829,1000 +LassoCV,LassoCV,partialling out,0.9,0.8930800000000001,0.37580933136906214,0.0927480396215142,0.998,0.9419266033822707,1000 +LassoCV,LassoCV,partialling out,0.95,0.94477,0.4478044383211074,0.0927480396215142,0.996,0.9487078274811828,1000 +LassoCV,RF Regr.,IV-type,0.9,0.8928400000000001,0.3592189643382743,0.08858475373164268,0.997,0.906279246304125,1000 +LassoCV,RF Regr.,IV-type,0.95,0.94447,0.42803579669984043,0.08858475373164268,0.999,0.9046683354643144,1000 +LassoCV,RF Regr.,partialling out,0.9,0.7744500000000001,0.4325567867931696,0.1411787082704541,0.985,1.0882496699575799,1000 +LassoCV,RF Regr.,partialling out,0.95,0.85524,0.515423202096265,0.1411787082704541,0.986,1.0866817530916593,1000 +RF Regr.,LassoCV,IV-type,0.9,0.88628,0.34773790027537904,0.08739450648497399,0.995,0.8767583563969938,1000 +RF Regr.,LassoCV,IV-type,0.95,0.93931,0.41435526507151715,0.08739450648497399,0.993,0.8726685703740463,1000 +RF Regr.,LassoCV,partialling out,0.9,0.86485,0.44479221962726684,0.11779311137598938,0.995,1.120671371140575,1000 +RF Regr.,LassoCV,partialling out,0.95,0.92655,0.5300026195575835,0.11779311137598938,0.994,1.1221894886367008,1000 +RF Regr.,RF Regr.,IV-type,0.9,0.8826,0.34326383534625277,0.08700990140042893,0.998,0.8628894725119298,1000 +RF Regr.,RF Regr.,IV-type,0.95,0.93355,0.409024087888394,0.08700990140042893,0.995,0.8641224623570899,1000 +RF Regr.,RF Regr.,partialling out,0.9,0.87676,0.38372252559163483,0.09828756016001841,0.996,0.9676080596207122,1000 +RF Regr.,RF Regr.,partialling out,0.95,0.93264,0.45723359081515513,0.09828756016001841,0.998,0.9616692545502734,1000 diff --git a/results/plm/plr_cate_metadata.csv b/results/plm/plr_cate_metadata.csv index 3527c916..3fb945c9 100644 --- a/results/plm/plr_cate_metadata.csv +++ b/results/plm/plr_cate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLRCATECoverageSimulation,2025-11-17 11:47,185.29402711788813,3.12.3,scripts/plm/plr_cate_config.yml +0.12.dev0,PLRCATECoverageSimulation,2025-12-04 20:18,189.09370460510254,3.12.3,scripts/plm/plr_cate_config.yml From 55c51bb60149e3372a10fe134384d4502d3963dc Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 20:23:10 +0000 Subject: [PATCH 45/51] Update results from script: scripts/plm/plr_ate.py --- results/plm/plr_ate_coverage.csv | 56 ++++++++++++++++---------------- results/plm/plr_ate_metadata.csv | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/results/plm/plr_ate_coverage.csv b/results/plm/plr_ate_coverage.csv index 3472d852..305cb658 100644 --- a/results/plm/plr_ate_coverage.csv +++ b/results/plm/plr_ate_coverage.csv @@ -1,29 +1,29 @@ Learner g,Learner m,Score,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Regr.,IV-type,0.9,0.863,0.1596875004155196,0.04190692285022562,1000 -LGBM Regr.,LGBM Regr.,IV-type,0.95,0.927,0.19027939293036994,0.04190692285022562,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.9,0.82,0.14673790477408832,0.04274003558423509,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.95,0.882,0.1748489979969301,0.04274003558423509,1000 -LGBM Regr.,LassoCV,IV-type,0.9,0.864,0.14850767996959235,0.039302736245328145,1000 -LGBM Regr.,LassoCV,IV-type,0.95,0.927,0.176957815211474,0.039302736245328145,1000 -LGBM Regr.,LassoCV,partialling out,0.9,0.882,0.15900684464932308,0.04075159325722853,1000 -LGBM Regr.,LassoCV,partialling out,0.95,0.932,0.189468341560354,0.04075159325722853,1000 -LassoCV,LGBM Regr.,IV-type,0.9,0.868,0.15035462431781724,0.03885008673020529,1000 -LassoCV,LGBM Regr.,IV-type,0.95,0.937,0.17915858514300867,0.03885008673020529,1000 -LassoCV,LGBM Regr.,partialling out,0.9,0.492,0.13884592445719834,0.0709498471401035,1000 -LassoCV,LGBM Regr.,partialling out,0.95,0.612,0.16544512343061302,0.0709498471401035,1000 -LassoCV,LassoCV,IV-type,0.9,0.869,0.13981178087085014,0.03694061273543505,1000 -LassoCV,LassoCV,IV-type,0.95,0.93,0.16659601233280855,0.03694061273543505,1000 -LassoCV,LassoCV,partialling out,0.9,0.883,0.14670738716893747,0.03669884399866198,1000 -LassoCV,LassoCV,partialling out,0.95,0.944,0.17481263402751057,0.03669884399866198,1000 -LassoCV,RF Regr.,IV-type,0.9,0.835,0.1302896143924338,0.03725192920475538,1000 -LassoCV,RF Regr.,IV-type,0.95,0.904,0.15524965114498646,0.03725192920475538,1000 -LassoCV,RF Regr.,partialling out,0.9,0.777,0.14256373927301522,0.046784627261935774,1000 -LassoCV,RF Regr.,partialling out,0.95,0.862,0.1698751730233513,0.046784627261935774,1000 -RF Regr.,LassoCV,IV-type,0.9,0.871,0.14106017506402294,0.036864349901817875,1000 -RF Regr.,LassoCV,IV-type,0.95,0.938,0.1680835657643333,0.036864349901817875,1000 -RF Regr.,LassoCV,partialling out,0.9,0.884,0.15071292176789794,0.03856536256116802,1000 -RF Regr.,LassoCV,partialling out,0.95,0.934,0.1795855228877442,0.03856536256116802,1000 -RF Regr.,RF Regr.,IV-type,0.9,0.83,0.13156454943211618,0.037590451309181865,1000 -RF Regr.,RF Regr.,IV-type,0.95,0.902,0.15676882994573899,0.037590451309181865,1000 -RF Regr.,RF Regr.,partialling out,0.9,0.875,0.14232152273160326,0.037118739561801554,1000 -RF Regr.,RF Regr.,partialling out,0.95,0.934,0.1695865542126264,0.037118739561801554,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.9,0.889,0.15978309138485988,0.040678761104429376,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.95,0.939,0.1903932965957687,0.040678761104429376,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.9,0.838,0.14681770731506133,0.041275148836012944,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.95,0.897,0.17494408858956342,0.041275148836012944,1000 +LGBM Regr.,LassoCV,IV-type,0.9,0.876,0.14833747639697978,0.039489073815308307,1000 +LGBM Regr.,LassoCV,IV-type,0.95,0.931,0.17675500514564524,0.039489073815308307,1000 +LGBM Regr.,LassoCV,partialling out,0.9,0.893,0.15937942718304723,0.03925857893846666,1000 +LGBM Regr.,LassoCV,partialling out,0.95,0.94,0.18991230103212867,0.03925857893846666,1000 +LassoCV,LGBM Regr.,IV-type,0.9,0.878,0.15066272181786255,0.03855046266735263,1000 +LassoCV,LGBM Regr.,IV-type,0.95,0.929,0.17952570595784667,0.03855046266735263,1000 +LassoCV,LGBM Regr.,partialling out,0.9,0.526,0.13905172633298185,0.06966076055623882,1000 +LassoCV,LGBM Regr.,partialling out,0.95,0.642,0.1656903514909566,0.06966076055623882,1000 +LassoCV,LassoCV,IV-type,0.9,0.877,0.13992145634265207,0.0366445077678926,1000 +LassoCV,LassoCV,IV-type,0.95,0.928,0.16672669871802667,0.0366445077678926,1000 +LassoCV,LassoCV,partialling out,0.9,0.897,0.14673266782178596,0.03648225251216086,1000 +LassoCV,LassoCV,partialling out,0.95,0.945,0.17484275778337358,0.03648225251216086,1000 +LassoCV,RF Regr.,IV-type,0.9,0.855,0.13032149928473868,0.037166678834479695,1000 +LassoCV,RF Regr.,IV-type,0.95,0.907,0.15528764433753836,0.037166678834479695,1000 +LassoCV,RF Regr.,partialling out,0.9,0.775,0.14270313357843983,0.04570634853982781,1000 +LassoCV,RF Regr.,partialling out,0.95,0.862,0.1700412715830077,0.04570634853982781,1000 +RF Regr.,LassoCV,IV-type,0.9,0.873,0.14101035943967402,0.03685517557846389,1000 +RF Regr.,LassoCV,IV-type,0.95,0.937,0.16802420678673716,0.03685517557846389,1000 +RF Regr.,LassoCV,partialling out,0.9,0.895,0.15051386498604236,0.037526282976942105,1000 +RF Regr.,LassoCV,partialling out,0.95,0.938,0.1793483321025444,0.037526282976942105,1000 +RF Regr.,RF Regr.,IV-type,0.9,0.837,0.13142137550510022,0.037870665143070414,1000 +RF Regr.,RF Regr.,IV-type,0.95,0.924,0.15659822768917436,0.037870665143070414,1000 +RF Regr.,RF Regr.,partialling out,0.9,0.884,0.1422661253784088,0.03699055268836001,1000 +RF Regr.,RF Regr.,partialling out,0.95,0.936,0.16952054419488322,0.03699055268836001,1000 diff --git a/results/plm/plr_ate_metadata.csv b/results/plm/plr_ate_metadata.csv index 50efb048..7c4c7b6b 100644 --- a/results/plm/plr_ate_metadata.csv +++ b/results/plm/plr_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLRATECoverageSimulation,2025-11-17 11:56,194.01051502227784,3.12.3,scripts/plm/plr_ate_config.yml +0.12.dev0,PLRATECoverageSimulation,2025-12-04 20:23,193.46772919098535,3.12.3,scripts/plm/plr_ate_config.yml From 586efca31f02be4b8e89f3e48f16f25da6ad74dd Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 20:44:38 +0000 Subject: [PATCH 46/51] Update results from script: scripts/did/did_cs_atte_coverage.py --- results/did/did_cs_atte_coverage_metadata.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/results/did/did_cs_atte_coverage_metadata.csv b/results/did/did_cs_atte_coverage_metadata.csv index 091e5c66..7cc6f839 100644 --- a/results/did/did_cs_atte_coverage_metadata.csv +++ b/results/did/did_cs_atte_coverage_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (seconds),Python Version -0.11.dev0,did_cs_atte_coverage.py,2025-09-08 10:19:25,13230.593134403229,3.12.3 +0.12.dev0,did_cs_atte_coverage.py,2025-12-04 20:44:32,12870.662700414658,3.12.3 From 05781963d4be165079da66e9bd83424f2c8a070d Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 20:58:13 +0000 Subject: [PATCH 47/51] Update results from script: scripts/plm/plr_ate_sensitivity.py --- results/plm/plr_ate_sensitivity_coverage.csv | 56 ++++++++++---------- results/plm/plr_ate_sensitivity_metadata.csv | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/results/plm/plr_ate_sensitivity_coverage.csv b/results/plm/plr_ate_sensitivity_coverage.csv index e37ff825..7eef0342 100644 --- a/results/plm/plr_ate_sensitivity_coverage.csv +++ b/results/plm/plr_ate_sensitivity_coverage.csv @@ -1,29 +1,29 @@ Learner g,Learner m,Score,level,Coverage,CI Length,Bias,Coverage (Lower),Coverage (Upper),RV,RVa,Bias (Lower),Bias (Upper),repetition -LGBM Regr.,LGBM Regr.,IV-type,0.9,0.359,1.3755861250755959,0.7533817675308617,1.0,0.983,0.10444003719792545,0.03319588065777179,1.4454211498085776,0.26049409728823314,1000 -LGBM Regr.,LGBM Regr.,IV-type,0.95,0.566,1.6391119663201015,0.7533817675308617,1.0,0.999,0.10444003719792545,0.018658743299259112,1.4454211498085776,0.26049409728823314,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.9,0.197,1.0680881308927423,0.7347354428810158,1.0,0.964,0.10198131802798086,0.0443414010278968,1.4282242316249965,0.25229952777929987,1000 -LGBM Regr.,LGBM Regr.,partialling out,0.95,0.324,1.2727055067777409,0.7347354428810158,1.0,0.992,0.10198131802798086,0.030840462791778402,1.4282242316249965,0.25229952777929987,1000 -LGBM Regr.,LassoCV,IV-type,0.9,0.015,1.4891617522492961,1.4488819159319037,1.0,0.378,0.18561743623652635,0.11029731701869455,2.1807608786019643,0.7221920339148978,1000 -LGBM Regr.,LassoCV,IV-type,0.95,0.042,1.7744456733044545,1.4488819159319037,1.0,0.601,0.18561743623652635,0.090030878628444,2.1807608786019643,0.7221920339148978,1000 -LGBM Regr.,LassoCV,partialling out,0.9,0.024,1.4917502052656801,1.3221204315783734,1.0,0.541,0.17228087019571867,0.09619454723796686,2.049489609674191,0.5997556153052517,1000 -LGBM Regr.,LassoCV,partialling out,0.95,0.073,1.7775300053110592,1.3221204315783734,1.0,0.761,0.17228087019571867,0.07583850420764672,2.049489609674191,0.5997556153052517,1000 -LassoCV,LGBM Regr.,IV-type,0.9,0.73,2.479560595815695,1.0346426864763705,1.0,1.0,0.06920738865206702,0.011540810801436563,2.5443047864136346,0.5569471732424347,1000 -LassoCV,LGBM Regr.,IV-type,0.95,0.893,2.954578684481825,1.0346426864763705,1.0,1.0,0.06920738865206702,0.0038983537654415056,2.5443047864136346,0.5569471732424347,1000 -LassoCV,LGBM Regr.,partialling out,0.9,0.632,1.9605167535420611,0.8992111659887163,1.0,1.0,0.05998295861547946,0.012000973566668068,2.4210030177077586,0.6440835936658005,1000 -LassoCV,LGBM Regr.,partialling out,0.95,0.835,2.336099799440206,0.8992111659887163,1.0,1.0,0.05998295861547946,0.004434166638410146,2.4210030177077586,0.6440835936658005,1000 -LassoCV,LassoCV,IV-type,0.9,0.0,2.5703372661953776,4.865260754822335,1.0,0.0,0.2830286662624298,0.22439945551293977,6.395774925585079,3.334746584059591,1000 -LassoCV,LassoCV,IV-type,0.95,0.0,3.062745758843568,4.865260754822335,1.0,0.0,0.2830286662624298,0.2078827373214068,6.395774925585079,3.334746584059591,1000 -LassoCV,LassoCV,partialling out,0.9,0.0,2.58639958016462,4.867314618361354,1.0,0.0,0.28309171431147057,0.22417951908385741,6.398064076377938,3.336565160344769,1000 -LassoCV,LassoCV,partialling out,0.95,0.0,3.0818851864329013,4.867314618361354,1.0,0.0,0.28309171431147057,0.2075756117099462,6.398064076377938,3.336565160344769,1000 -LassoCV,RF Regr.,IV-type,0.9,0.03,2.201968880538331,1.7117879345092915,1.0,0.994,0.10304348206977468,0.05118734638448726,3.365492564732171,0.3138790534461325,1000 -LassoCV,RF Regr.,IV-type,0.95,0.099,2.623807754208416,1.7117879345092915,1.0,1.0,0.10304348206977468,0.0369081382284807,3.365492564732171,0.3138790534461325,1000 -LassoCV,RF Regr.,partialling out,0.9,0.033,2.2330906910397963,1.656734265754782,1.0,0.998,0.0982817284966058,0.04650603557240649,3.3380373315922904,0.30299233007013754,1000 -LassoCV,RF Regr.,partialling out,0.95,0.13,2.6608916787091044,1.656734265754782,1.0,0.999,0.0982817284966058,0.032237308880055986,3.3380373315922904,0.30299233007013754,1000 -RF Regr.,LassoCV,IV-type,0.9,0.001,1.951602488934002,2.496369543889771,1.0,0.149,0.1882495178000821,0.13226664609458022,3.749193574131233,1.2443559351590099,1000 -RF Regr.,LassoCV,IV-type,0.95,0.004,2.325477798008481,2.496369543889771,1.0,0.283,0.1882495178000821,0.11629352945863138,3.749193574131233,1.2443559351590099,1000 -RF Regr.,LassoCV,partialling out,0.9,0.002,1.9227923312991766,2.18289621236076,1.0,0.321,0.16667394665087393,0.11098639679899193,3.4384927296936114,0.928664433523872,1000 -RF Regr.,LassoCV,partialling out,0.95,0.01,2.291148377792633,2.18289621236076,1.0,0.566,0.16667394665087393,0.09505438614556037,3.4384927296936114,0.928664433523872,1000 -RF Regr.,RF Regr.,IV-type,0.9,0.015,1.7421619533890125,1.6093123421586957,1.0,0.903,0.1189984467466679,0.06912583869905706,2.9380563428464184,0.3931839897902748,1000 -RF Regr.,RF Regr.,IV-type,0.95,0.051,2.0759140071368507,1.6093123421586957,1.0,0.967,0.1189984467466679,0.05511967656630338,2.9380563428464184,0.3931839897902748,1000 -RF Regr.,RF Regr.,partialling out,0.9,0.013,1.7368870337302156,1.5939873207774409,1.0,0.931,0.1174689960814369,0.06795129356233286,2.9287971865967988,0.37330009521026997,1000 -RF Regr.,RF Regr.,partialling out,0.95,0.047,2.0696285526847444,1.5939873207774409,1.0,0.976,0.1174689960814369,0.05405509596164712,2.9287971865967988,0.37330009521026997,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.9,0.421,1.3976511350473353,0.7467964250024092,1.0,0.987,0.10290839957959286,0.031490519586569884,1.4404253946326935,0.2795318845446421,1000 +LGBM Regr.,LGBM Regr.,IV-type,0.95,0.603,1.6654040473627647,0.7467964250024092,1.0,0.994,0.10290839957959286,0.017696866745516855,1.4404253946326935,0.2795318845446421,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.9,0.192,1.102290427161417,0.7410606150892608,1.0,0.974,0.10272589638995315,0.04391069909187329,1.4345048247464856,0.251626505274024,1000 +LGBM Regr.,LGBM Regr.,partialling out,0.95,0.322,1.313460056469443,0.7410606150892608,1.0,0.995,0.10272589638995315,0.03006796975885084,1.4345048247464856,0.251626505274024,1000 +LGBM Regr.,LassoCV,IV-type,0.9,0.014,1.509085669359172,1.4539598882028426,1.0,0.366,0.18611240379020022,0.11033278480181255,2.1857839931874423,0.7279169255468876,1000 +LGBM Regr.,LassoCV,IV-type,0.95,0.046,1.7981864848432254,1.4539598882028426,1.0,0.594,0.18611240379020022,0.08990359936209669,2.1857839931874423,0.7279169255468876,1000 +LGBM Regr.,LassoCV,partialling out,0.9,0.035,1.507921245387843,1.3159744755105507,1.0,0.549,0.17172124099290512,0.0951449003773672,2.0419784435742567,0.5943030774757848,1000 +LGBM Regr.,LassoCV,partialling out,0.95,0.08,1.7967989881023936,1.3159744755105507,1.0,0.771,0.17172124099290512,0.0748164416512186,2.0419784435742567,0.5943030774757848,1000 +LassoCV,LGBM Regr.,IV-type,0.9,0.726,2.4983280456112573,1.026175519781805,1.0,1.0,0.0687018781764579,0.01120762678999437,2.535249747799531,0.567952628278145,1000 +LassoCV,LGBM Regr.,IV-type,0.95,0.903,2.976941480221371,1.026175519781805,1.0,1.0,0.0687018781764579,0.0036246937120926154,2.535249747799531,0.567952628278145,1000 +LassoCV,LGBM Regr.,partialling out,0.9,0.607,1.966778043542478,0.9043657907050859,1.0,1.0,0.06031903912908668,0.012377995755699906,2.426212574731265,0.6422721718417567,1000 +LassoCV,LGBM Regr.,partialling out,0.95,0.832,2.3435605866473455,0.9043657907050859,1.0,1.0,0.06031903912908668,0.004604329422567183,2.426212574731265,0.6422721718417567,1000 +LassoCV,LassoCV,IV-type,0.9,0.0,2.571731206770743,4.8737377482499245,1.0,0.0,0.28348151920253006,0.22518977741829715,6.4038522831761675,3.343623213323681,1000 +LassoCV,LassoCV,IV-type,0.95,0.0,3.0644067414863634,4.8737377482499245,1.0,0.001,0.28348151920253006,0.208728038503683,6.4038522831761675,3.343623213323681,1000 +LassoCV,LassoCV,partialling out,0.9,0.0,2.5851741127390566,4.874876663212447,1.0,0.0,0.28345112594648986,0.22493068632003588,6.405508112416897,3.344245214007996,1000 +LassoCV,LassoCV,partialling out,0.95,0.0,3.0804249519299787,4.874876663212447,1.0,0.001,0.28345112594648986,0.20839886362292398,6.405508112416897,3.344245214007996,1000 +LassoCV,RF Regr.,IV-type,0.9,0.037,2.2221799551660677,1.7160440423474188,1.0,0.996,0.10324506511335448,0.050995875886257494,3.369265659703337,0.3191854379820456,1000 +LassoCV,RF Regr.,IV-type,0.95,0.102,2.647890735034274,1.7160440423474188,1.0,1.0,0.10324506511335448,0.0366867084756369,3.369265659703337,0.3191854379820456,1000 +LassoCV,RF Regr.,partialling out,0.9,0.047,2.2521751990434016,1.6622447383497525,1.0,0.999,0.09850976200308284,0.04641917011881089,3.343887329180081,0.29505795733293616,1000 +LassoCV,RF Regr.,partialling out,0.95,0.128,2.6836322726056316,1.6622447383497525,1.0,1.0,0.09850976200308284,0.0322205738296689,3.343887329180081,0.29505795733293616,1000 +RF Regr.,LassoCV,IV-type,0.9,0.001,1.9711202298359545,2.4918741008270446,1.0,0.144,0.1879831825557554,0.1316596199671192,3.7439258798736605,1.2404389514158414,1000 +RF Regr.,LassoCV,IV-type,0.95,0.004,2.348734620743712,2.4918741008270446,1.0,0.291,0.1879831825557554,0.11556104506319523,3.7439258798736605,1.2404389514158414,1000 +RF Regr.,LassoCV,partialling out,0.9,0.005,1.9387654117166035,2.180283691840108,1.0,0.33,0.16645777455701985,0.11047766645086829,3.435461938620182,0.9269126964214449,1000 +RF Regr.,LassoCV,partialling out,0.95,0.009,2.3101814770467843,2.180283691840108,1.0,0.565,0.16645777455701985,0.09449083263428158,3.435461938620182,0.9269126964214449,1000 +RF Regr.,RF Regr.,IV-type,0.9,0.026,1.7732461193193343,1.6256342448094874,1.0,0.896,0.12001444083856587,0.0694389840407145,2.9550164404884054,0.4058056479339932,1000 +RF Regr.,RF Regr.,IV-type,0.95,0.061,2.1129530753643446,1.6256342448094874,1.0,0.966,0.12001444083856587,0.0553030741582177,2.9550164404884054,0.4058056479339932,1000 +RF Regr.,RF Regr.,partialling out,0.9,0.025,1.766885805915271,1.5903668070720287,1.0,0.918,0.11721222343827108,0.06696849029772275,2.923581829825191,0.38139692141605425,1000 +RF Regr.,RF Regr.,partialling out,0.95,0.072,2.1053742944940645,1.5903668070720287,1.0,0.976,0.11721222343827108,0.05288881709860531,2.923581829825191,0.38139692141605425,1000 diff --git a/results/plm/plr_ate_sensitivity_metadata.csv b/results/plm/plr_ate_sensitivity_metadata.csv index ba728b1b..f14be3df 100644 --- a/results/plm/plr_ate_sensitivity_metadata.csv +++ b/results/plm/plr_ate_sensitivity_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLRATESensitivityCoverageSimulation,2025-11-17 12:27,224.5860997915268,3.12.3,scripts/plm/plr_ate_sensitivity_config.yml +0.12.dev0,PLRATESensitivityCoverageSimulation,2025-12-04 20:58,228.5057456254959,3.12.3,scripts/plm/plr_ate_sensitivity_config.yml From c82ba446c3e7a16de8602bab10cadee15a841eb2 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 21:12:45 +0000 Subject: [PATCH 48/51] Update results from script: scripts/did/did_pa_atte_coverage.py --- results/did/did_pa_atte_coverage.csv | 6 +++--- results/did/did_pa_atte_coverage_metadata.csv | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/results/did/did_pa_atte_coverage.csv b/results/did/did_pa_atte_coverage.csv index 9119e34e..4d165627 100644 --- a/results/did/did_pa_atte_coverage.csv +++ b/results/did/did_pa_atte_coverage.csv @@ -25,13 +25,13 @@ LGBM,LGBM,experimental,True,6,0.9,0.897,1.8093537325759845,0.43669034939483387,1 LGBM,LGBM,experimental,True,6,0.95,0.944,2.1559779502779253,0.43669034939483387,1000 LGBM,LGBM,observational,False,1,0.9,0.893,12.590652453419517,3.3787304616783773,1000 LGBM,LGBM,observational,False,1,0.95,0.953,15.002687744501154,3.3787304616783773,1000 -LGBM,LGBM,observational,False,2,0.9,0.914,14.716645515368727,3.622038823656133,1000 -LGBM,LGBM,observational,False,2,0.95,0.966,17.535964727040476,3.622038823656133,1000 +LGBM,LGBM,observational,False,2,0.9,0.914,14.716645515368729,3.6220388236561334,1000 +LGBM,LGBM,observational,False,2,0.95,0.966,17.535964727040476,3.6220388236561334,1000 LGBM,LGBM,observational,False,3,0.9,0.933,14.387879061625718,3.413516510279929,1000 LGBM,LGBM,observational,False,3,0.95,0.977,17.14421533481309,3.413516510279929,1000 LGBM,LGBM,observational,False,4,0.9,0.843,18.129751335736472,5.765050835726839,1000 LGBM,LGBM,observational,False,4,0.95,0.932,21.602931157204278,5.765050835726839,1000 -LGBM,LGBM,observational,False,5,0.9,0.917,7.704465948378402,1.901185439754437,1000 +LGBM,LGBM,observational,False,5,0.9,0.917,7.704465948378403,1.901185439754437,1000 LGBM,LGBM,observational,False,5,0.95,0.96,9.180437414922883,1.901185439754437,1000 LGBM,LGBM,observational,False,6,0.9,0.922,7.569553123736534,1.7987428999886972,1000 LGBM,LGBM,observational,False,6,0.95,0.971,9.019678868984235,1.7987428999886972,1000 diff --git a/results/did/did_pa_atte_coverage_metadata.csv b/results/did/did_pa_atte_coverage_metadata.csv index 000a12d0..ba65af64 100644 --- a/results/did/did_pa_atte_coverage_metadata.csv +++ b/results/did/did_pa_atte_coverage_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (seconds),Python Version -0.11.dev0,did_pa_atte_coverage.py,2025-09-08 09:48:43,11386.774159193039,3.12.3 +0.12.dev0,did_pa_atte_coverage.py,2025-12-04 21:12:43,14567.57974767685,3.12.3 From a0a59f04de3772da25d14b39cbb01c7a995b7b3e Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 21:25:13 +0000 Subject: [PATCH 49/51] Update results from script: scripts/ssm/ssm_mar_ate.py --- results/ssm/ssm_mar_ate_coverage.csv | 36 ++++++++++++++-------------- results/ssm/ssm_mar_ate_metadata.csv | 2 +- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/results/ssm/ssm_mar_ate_coverage.csv b/results/ssm/ssm_mar_ate_coverage.csv index 0d322671..86a35c6b 100644 --- a/results/ssm/ssm_mar_ate_coverage.csv +++ b/results/ssm/ssm_mar_ate_coverage.csv @@ -1,19 +1,19 @@ Learner g,Learner m,Learner pi,level,Coverage,CI Length,Bias,repetition -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.961,1.099744720107085,0.23879351817688696,1000 -LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.989,1.310426659418225,0.23879351817688696,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.9,0.954,0.9376788712007889,0.2071243110281803,1000 -LGBM Regr.,LGBM Clas.,Logistic,0.95,0.985,1.1173132894650808,0.2071243110281803,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.9,0.946,0.7825401174653722,0.1661015304067069,1000 -LGBM Regr.,Logistic,LGBM Clas.,0.95,0.985,0.9324540625128358,0.1661015304067069,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.952,1.0334523729357563,0.21861638917209628,1000 -LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.984,1.2314344556272774,0.21861638917209628,1000 -LassoCV,Logistic,Logistic,0.9,0.934,0.5897223415076497,0.13227237497121175,1000 -LassoCV,Logistic,Logistic,0.95,0.974,0.7026975113741984,0.13227237497121175,1000 -LassoCV,RF Clas.,RF Clas.,0.9,0.938,0.5150861645399253,0.11241955689454874,1000 -LassoCV,RF Clas.,RF Clas.,0.95,0.976,0.6137630211535599,0.11241955689454874,1000 -RF Regr.,Logistic,RF Clas.,0.9,0.93,0.5750069155584657,0.13119995868541565,1000 -RF Regr.,Logistic,RF Clas.,0.95,0.97,0.6851629998498987,0.13119995868541565,1000 -RF Regr.,RF Clas.,Logistic,0.9,0.932,0.5561984627007869,0.12247623339858656,1000 -RF Regr.,RF Clas.,Logistic,0.95,0.963,0.6627513459483337,0.12247623339858656,1000 -RF Regr.,RF Clas.,RF Clas.,0.9,0.928,0.5232206377007457,0.11739694066484004,1000 -RF Regr.,RF Clas.,RF Clas.,0.95,0.967,0.6234558437653592,0.11739694066484004,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.9,0.932,1.1096490607618295,0.2537753697730454,1000 +LGBM Regr.,LGBM Clas.,LGBM Clas.,0.95,0.973,1.3222284092249192,0.2537753697730454,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.9,0.927,0.9202034782128122,0.21795635797590815,1000 +LGBM Regr.,LGBM Clas.,Logistic,0.95,0.98,1.0964900743711041,0.21795635797590815,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.9,0.925,0.7859610293391193,0.17778968043589227,1000 +LGBM Regr.,Logistic,LGBM Clas.,0.95,0.97,0.9365303304293047,0.17778968043589227,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.9,0.937,1.06664811597998,0.24252593303321004,1000 +LassoCV,LGBM Clas.,LGBM Clas.,0.95,0.978,1.2709896231757172,0.24252593303321004,1000 +LassoCV,Logistic,Logistic,0.9,0.929,0.5870360234747519,0.1352163741591098,1000 +LassoCV,Logistic,Logistic,0.95,0.966,0.6994965660078569,0.1352163741591098,1000 +LassoCV,RF Clas.,RF Clas.,0.9,0.912,0.5180218173196215,0.12230587140635803,1000 +LassoCV,RF Clas.,RF Clas.,0.95,0.957,0.6172610671954945,0.12230587140635803,1000 +RF Regr.,Logistic,RF Clas.,0.9,0.907,0.5844857310821764,0.1396924993073941,1000 +RF Regr.,Logistic,RF Clas.,0.95,0.958,0.6964577051891234,0.1396924993073941,1000 +RF Regr.,RF Clas.,Logistic,0.9,0.917,0.5605579339809742,0.12931631817619357,1000 +RF Regr.,RF Clas.,Logistic,0.95,0.962,0.6679459763767203,0.12931631817619357,1000 +RF Regr.,RF Clas.,RF Clas.,0.9,0.91,0.5264596768137552,0.12286829856248617,1000 +RF Regr.,RF Clas.,RF Clas.,0.95,0.953,0.6273153969207249,0.12286829856248617,1000 diff --git a/results/ssm/ssm_mar_ate_metadata.csv b/results/ssm/ssm_mar_ate_metadata.csv index 7a2e3692..88c4652d 100644 --- a/results/ssm/ssm_mar_ate_metadata.csv +++ b/results/ssm/ssm_mar_ate_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,SSMMarATECoverageSimulation,2025-09-08 10:47,249.12814004421233,3.12.3,scripts/ssm/ssm_mar_ate_config.yml +0.12.dev0,SSMMarATECoverageSimulation,2025-12-04 21:25,255.4998017311096,3.12.3,scripts/ssm/ssm_mar_ate_config.yml From 3fcf8a27fb41708b8d1d6d60df347ca706cd8533 Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 22:25:53 +0000 Subject: [PATCH 50/51] Update results from script: scripts/did/did_cs_multi.py --- results/did/did_cs_multi_detailed.csv | 96 ++++++++++++------------- results/did/did_cs_multi_eventstudy.csv | 96 ++++++++++++------------- results/did/did_cs_multi_group.csv | 96 ++++++++++++------------- results/did/did_cs_multi_metadata.csv | 2 +- results/did/did_cs_multi_time.csv | 96 ++++++++++++------------- 5 files changed, 193 insertions(+), 193 deletions(-) diff --git a/results/did/did_cs_multi_detailed.csv b/results/did/did_cs_multi_detailed.csv index e36dbf2b..39ccee5d 100644 --- a/results/did/did_cs_multi_detailed.csv +++ b/results/did/did_cs_multi_detailed.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_d0_t0,Loss g_d1_t0,Loss g_d0_t1,Loss g_d1_t1,Loss m,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.8948333333333334,3.2830873544118973,0.7905246305869522,0.878,5.150445591303714,6.529012705055243,5.971591086257874,10.328170306432364,9.338187819987848,,500 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.943,3.912039872308984,0.7905246305869522,0.918,5.647198685341647,6.529012705055243,5.971591086257874,10.328170306432364,9.338187819987848,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.8888333333333334,2.844466891799001,0.6885591812636256,0.854,4.471361471167187,5.49672067934481,5.5627529789128,8.596569266811002,8.692269838482467,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.9426666666666667,3.389391354825466,0.6885591812636256,0.93,4.894219128111993,5.49672067934481,5.5627529789128,8.596569266811002,8.692269838482467,,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.9,0.9556666666666667,2.879356008655542,0.563136502516892,0.976,4.5265237334540265,5.568585668370725,5.5708138758932755,8.717635002907942,8.745078576056867,,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.982,3.4309643017252864,0.563136502516892,0.99,4.956034625046507,5.568585668370725,5.5708138758932755,8.717635002907942,8.745078576056867,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.9,0.8898333333333334,3.2855480715558047,0.7925255864973799,0.864,5.148118901661281,6.529908690098191,5.973881547103242,10.330575563495493,9.339187189951378,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.95,0.9466666666666667,3.9149719976356234,0.7925255864973799,0.93,5.644728532586285,6.529908690098191,5.973881547103242,10.330575563495493,9.339187189951378,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.9,0.8911666666666667,2.8465845975978494,0.6902411381722026,0.862,4.480023796591902,5.496857322731734,5.563946426541767,8.598689315021057,8.692831647041208,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.95,0.9458333333333334,3.3919147569249515,0.6902411381722026,0.914,4.9050776614464,5.496857322731734,5.563946426541767,8.598689315021057,8.692831647041208,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.9,0.9566666666666667,2.879829540706673,0.5621215752155799,0.982,4.526560646254027,5.5658080803951355,5.566873062122812,8.7223284024544,8.73466619111826,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.95,0.9853333333333334,3.4315285499663064,0.5621215752155799,0.992,4.9551572605720136,5.5658080803951355,5.566873062122812,8.7223284024544,8.73466619111826,,500 -LGBM Regr.,LGBM 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g_d0_t0,Loss g_d1_t0,Loss g_d0_t1,Loss g_d1_t1,Loss m,repetition -LGBM Regr.,LGBM Clas.,experimental,False,1,0.9,0.8576666666666666,2.3174152566788706,0.661019246433259,0.852,3.323210464931247,6.529012705055243,5.971591086257874,10.328170306432364,9.338187819987848,,500 -LGBM Regr.,LGBM Clas.,experimental,False,1,0.95,0.9236666666666666,2.7613705960768966,0.661019246433259,0.904,3.684353000792572,6.529012705055243,5.971591086257874,10.328170306432364,9.338187819987848,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.9,0.8633333333333334,2.017204380802178,0.5713151828489281,0.84,2.9002651834190485,5.49672067934481,5.5627529789128,8.596569266811002,8.692269838482467,,500 -LGBM Regr.,LGBM Clas.,experimental,False,4,0.95,0.926,2.403647273560915,0.5713151828489281,0.902,3.212908760146589,5.49672067934481,5.5627529789128,8.596569266811002,8.692269838482467,,500 -LGBM Regr.,LGBM 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+Linear,Logistic,observational,True,4,0.9,0.8626666666666666,5.178515931845214,1.2461513670243192,0.834,6.640015577975941,7.317555311981951,7.362601753308157,11.670615292305248,11.711325063822715,0.6729405659549627,500 +Linear,Logistic,observational,True,4,0.95,0.912,6.170582326279506,1.2461513670243192,0.9,7.5044529911531725,7.317555311981951,7.362601753308157,11.670615292305248,11.711325063822715,0.6729405659549627,500 +Linear,Logistic,observational,True,6,0.9,0.9566666666666667,4.884149276989282,0.9018881464166801,0.976,6.26232727716403,7.559243151805225,7.5771914622592105,12.019363662222768,12.096028014111692,0.6939192119435217,500 +Linear,Logistic,observational,True,6,0.95,0.9853333333333334,5.819822822629045,0.9018881464166801,0.988,7.079344135796427,7.559243151805225,7.5771914622592105,12.019363662222768,12.096028014111692,0.6939192119435217,500 diff --git a/results/did/did_cs_multi_metadata.csv b/results/did/did_cs_multi_metadata.csv index e4260cdf..ce5e6888 100644 --- a/results/did/did_cs_multi_metadata.csv +++ b/results/did/did_cs_multi_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.12.dev0,DIDCSMultiCoverageSimulation,2025-12-03 19:06,36.865641951560974,3.12.9,scripts/did/did_cs_multi_config.yml +0.12.dev0,DIDCSMultiCoverageSimulation,2025-12-04 22:25,315.6724048376083,3.12.3,scripts/did/did_cs_multi_config.yml diff --git a/results/did/did_cs_multi_time.csv b/results/did/did_cs_multi_time.csv index fc3aa48c..80684b8b 100644 --- a/results/did/did_cs_multi_time.csv +++ b/results/did/did_cs_multi_time.csv @@ -1,49 +1,49 @@ Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,Loss g_d0_t0,Loss g_d1_t0,Loss g_d0_t1,Loss g_d1_t1,Loss m,repetition -LGBM Regr.,LGBM 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Clas.,experimental,False,6,0.9,0.9593333333333334,2.715378064864802,0.5341667940093989,0.966,3.4860667618900516,5.568585668370725,5.5708138758932755,8.717635002907942,8.745078576056867,,500 -LGBM Regr.,LGBM Clas.,experimental,False,6,0.95,0.9833333333333334,3.235572530188483,0.5341667940093989,0.994,3.9418062204960087,5.568585668370725,5.5708138758932755,8.717635002907942,8.745078576056867,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.9,0.896,3.117817590568382,0.7633116622668772,0.91,4.004047606026155,6.529908690098191,5.973881547103242,10.330575563495493,9.339187189951378,,500 -LGBM Regr.,LGBM Clas.,experimental,True,1,0.95,0.9506666666666667,3.7151088022373693,0.7633116622668772,0.95,4.525836455097325,6.529908690098191,5.973881547103242,10.330575563495493,9.339187189951378,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.9,0.88,2.6556749636180674,0.6994633089694186,0.888,3.406720349076596,5.496857322731734,5.563946426541767,8.598689315021057,8.692831647041208,,500 -LGBM Regr.,LGBM Clas.,experimental,True,4,0.95,0.944,3.1644318971913563,0.6994633089694186,0.944,3.8522516407691674,5.496857322731734,5.563946426541767,8.598689315021057,8.692831647041208,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.9,0.9533333333333334,2.7153400654764255,0.542891225253555,0.97,3.4903725341103025,5.5658080803951355,5.566873062122812,8.7223284024544,8.73466619111826,,500 -LGBM Regr.,LGBM Clas.,experimental,True,6,0.95,0.98,3.235527251124479,0.542891225253555,0.994,3.9361949699158685,5.5658080803951355,5.566873062122812,8.7223284024544,8.73466619111826,,500 -LGBM Regr.,LGBM Clas.,observational,False,1,0.9,0.9533333333333334,13.060460366432823,2.8342360320821194,0.966,16.74617642211609,6.539994802184515,5.973269082413658,10.326203789622623,9.333269582636964,0.6228342742079863,500 -LGBM Regr.,LGBM 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+Linear,Logistic,observational,True,6,0.9,0.96,4.224826442302571,0.7755018492012593,0.978,5.4337222374310015,7.559243151805225,7.5771914622592105,12.019363662222768,12.096028014111692,0.6939192119435217,500 +Linear,Logistic,observational,True,6,0.95,0.986,5.034191208364479,0.7755018492012593,0.996,6.131887010616248,7.559243151805225,7.5771914622592105,12.019363662222768,12.096028014111692,0.6939192119435217,500 From 17d9360ca867ce091bb23cdf12be04c6b5b73d1a Mon Sep 17 00:00:00 2001 From: github-actions Date: Thu, 4 Dec 2025 22:43:46 +0000 Subject: [PATCH 51/51] Update results from script: scripts/plm/pliv_late.py --- results/plm/pliv_late_coverage.csv | 64 +++++++++++++++--------------- results/plm/pliv_late_metadata.csv | 2 +- 2 files changed, 33 insertions(+), 33 deletions(-) diff --git a/results/plm/pliv_late_coverage.csv b/results/plm/pliv_late_coverage.csv index c606e9a8..826271f0 100644 --- a/results/plm/pliv_late_coverage.csv +++ b/results/plm/pliv_late_coverage.csv @@ -1,33 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out,0.95,0.9148936170212766,0.35454897447776274,0.0783357095787531,564 diff --git a/results/plm/pliv_late_metadata.csv b/results/plm/pliv_late_metadata.csv index 9932a0ae..fc3b2481 100644 --- a/results/plm/pliv_late_metadata.csv +++ b/results/plm/pliv_late_metadata.csv @@ -1,2 +1,2 @@ DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File -0.11.dev0,PLIVLATECoverageSimulation,2025-09-08 12:11,332.7484830458959,3.12.3,scripts/plm/pliv_late_config.yml +0.12.dev0,PLIVLATECoverageSimulation,2025-12-04 22:43,334.08844143152237,3.12.3,scripts/plm/pliv_late_config.yml