Curated resources for causal inference and experimentation

Code and data for my article 'The Economist's Guide to Causal Forests'

Causal Inference Engine using T-Learners (XGBoost) to optimize marketing ROI. Features: 3.2x Lift over random targeting, Behavior-Based Segmentation (Persuadables vs. Sleeping Dogs), and fully dockerized FastAPI/Streamlit architecture.

"Causal Machine Learning for Cost-Effective Allocation of Electricity Aid" thesis for my Masters in Management and Digital Technologies at Ludwig-Maximillian Univeristy, Munich.

OLS on observational data says job training hurts earnings. Double ML corrects the bias and recovers the $1,794 RCT ground truth. Per-individual CATE · SHAP moderators · FastAPI · Streamlit dashboard.

Causal ML for drug discovery: treatment effect estimation, causal graphs, propensity score methods, and perturbation response prediction.

Reliable and Fair Causal Machine Learning for Sparse Subpopulations in NSDUH 2021–2023

Chapter wise source code for Causal Machine Learning book by Durai Rajamanickam

Causal bandit orchestration platform for real-time adaptive experimentation, sequential testing, and interference-aware decisioning.

CANS: Production-ready causal inference with GNNs, Transformers, CFRNet and LLM integration. The most comprehensive causal AI framework.

Causal analysis framework using Double Machine Learning to quantitatively isolate the effect of model size on deep learning performance while controlling for confounders such as dataset size, training time, and hyperparameters.

Judea Pearl’s Causal Ladder, featuring Association, Intervention, and Counterfactual models.

Causal-AIRL: MSc research code + interactive demo. 23pp↑ cross-style policy agreement via latent Z deconfounding. MSc Data Science @ Edinburgh 2024-25.