Standard syllabus
Causal inference · Graduate · Statistics
Topics
Potential outcomes framework
- Treatment effects: ATE, ATT, and CATE
- Randomized experiments as gold standard
- SUTVA and consistency assumptions
- Bias decomposition: confounding and selection
- DAGs for causal identification (introduction)
Quasi-experimental methods
- Matching and propensity scores
- Inverse probability weighting
- Difference-in-differences
- Regression discontinuity designs
- Instrumental variables for causal effects
Advanced identification
- Mediation analysis (introduction)
- Sensitivity analysis for unmeasured confounding
- Synthetic control methods (overview)
- Causal inference with time-varying treatments (intro)
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.