Theoretical / proof-based
Causal inference · Graduate · Statistics
Topics
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
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.