Causal Inference

3 credits · Graduate School

This advanced course introduces causal inference methods for observational data and randomized studies. Topics include the Rubin causal model, directed acyclic graphs, propensity scores, inverse probability weighting, instrumental variables, causal mediation analysis, marginal structural models, g-computation, and sensitivity analyses to examine robustness to untestable assumptions. Students learn the basic theory behind the methods and their application to biomedical data examples. Prerequisites: BIOS 6312 and BIOS 6342. [3].