Models for Causal Inference – EPI289
Instructor: Miguel Hernán
EPI289 describes models for causal inference, their assumptions, and their practical application to epidemiologic data. The course introduces outcome regression, propensity score methods, the parametric g-formula, inverse probability weighting of marginal structural models, g-estimation of nested structural models, and instrumental variable methods. The course also introduces models for causal inference in the presence of time-varying exposures, which will be extensively studied in EPI207. EPI289 is designed to be taken after EPI201/EPI202. The epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289. Familiarity with the SAS language is strongly recommended.
Spring Semester, Harvard School of Public Health