Causal inference
Graduate · Statistics
Syllabus focus
Standard syllabus · Theoretical / proof-based
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
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)
Theoretical / proof-based
Formal causal theory
- Identification vs estimation
- Proofs of unbiasedness under assumptions
- Semiparametric efficiency bounds
- Double/debiased machine learning (introduction)
- Causal graphs and d-separation
- Nonparametric identification results (overview)
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Reflects modern graduate causal inference curricula (Imbens & Rubin, Hernán & Robins). Mix of theory and applied quasi-experimental methods.