Statistics and Experiments Section 017
Causal Inference Basics
Working out what actually causes what, when you cannot run the experiment you wish you could.
13 of 13 lessons published Three reading levels on every lesson
Lessons in order
Work top to bottom. Each lesson assumes the one above it.
- Why a great model answers the wrong question
- Potential outcomes and counterfactuals
- Confounding
- Causal DAGs and what to control for
- Colliders and selection bias
- Simpson's paradox
- Propensity score matching
- Inverse probability weighting
- Difference-in-differences
- Regression discontinuity
- Instrumental variables
- Uplift modelling
- Double machine learning