Causal Inference

3 Credits · Level: Graduate

This course introduces concepts and methods for causal inference including potential outcomes, directed acyclic graphs, confounding, selction bias, propensity score methods, inverse probability weighting of marginal structural models, parametric g-formula, g-estimation of structural nested models, mediation analysis, methods to handle unmeasured confounding, and methods to deal with time-dependent confounding.