Causal Inference

This repository is notes from the Udacity Course Causal Inference, conducted by Jonathan Hershaff. The original notebooks from this class is also available here on github.

1. Correlation vs Causation
2. Two Languages of Causality: DAG and Potential Outcomes Framework
3. Interrupted Time Series
4. Interrupted Time Series Exercise
5. Difference in Differences (DiD)
6. DiD exercise
7. Pre-event validation and testing
8. Test DiD assumptions
9. Placebo Test
10. Placebo test with DiD
11. Event Studies
12. Event Studies Exercise
13. Modeling Time Varying Effects
14. EVent Study Model Validation
15. Event Study Lead Lag Coefficients Exercise
16. Event Study with Staggered Rollout
17. Staggered Rollout (Dividents example) Exercise
18. Synthetic Control with Lasso
19. Synthetic Control Exercise
20. Model Validation with Simulated Data
21. Compare Models
22. Bayesian Structural Time Series Causallimpact in R
23. Regression Discontinuity
24. Regression Discontinuity Exercise
25. Regression Discontinuity Model Validation and Sensitivity of Bandwidth
26. Regression Discontinuity Model Validation Exercise
27. Communicating Non-Experimental Causal Results
28. Presenting Results to Stakeholders Example
29. Final Project