Foundations for Causal Inference

3 units

This course offers a comprehensive introduction to the concepts, methods, and applications of causal inference in the social and biomedical sciences. Topics include the potential outcomes framework, assumptions for causal identification, and a range of statistical approaches for estimating various causal effects in both experimental and observational studies, such as propensity score methods, doubly robust estimators, g-computation methods, instrumental variables estimation, and double machine learning estimators. Students will learn to critically evaluate study designs, assess the plausibility of assumptions, and interpret results in context. The course emphasizes both statistical foundations and practical applications, with hands-on exercises in R for implementing methods and analyzing real-world datasets.