Causal Inference in Clinical Trials and Observational Study (II)

Catalog description

This course offers an overview of statistical foundations for causal inference. This course introduces new analytic methods for causal inference in observational study including propensity score, doubly robust estimation, instrumental variables, marginal structure modeling for time-varying confounding, precision medicine, and sensitivity analysis for unmeasured confounding. This course also offers study design issues such as estimand. The course is designed to be a continuation of the clinical trial course (BIODS 248) and focuses on making causal inferences via observational study including real world data. However, BIODS 248 is not required for this course, which is self-contained. Prerequisites: Working knowledge of statistical inference, probability theory, and R.