Causal Inference in Clinical Trials and Observational Studies (STATS 251)

3 credits · Graduate

This course provides a rigorous overview of the statistical foundations of causal inference and introduces modern analytic methods for estimating causal effects in randomized trials and observational studies. Topics include outcome regression, propensity-score methods, doubly robust estimators, instrumental variables, approaches for estimating heterogeneous treatment effects (useful for precision medicine), marginal structural models to handle time-varying confounding, and sensitivity analyses for unmeasured confounding; the course also covers study-design considerations such as estimand choice and adaptive randomization. BMDS250 on clinical trial design is a helpful complement but not required. Prerequisites: working knowledge of statistical inference, probability theory, and R.In addition, for both BMDS 250 and BMDS251, instructors should be Ying Lu (PI) and Lu Tian (PI).