This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: STATS116/STATS 118, STATS STATS 191/STATS 203. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
3 units · Letter or Credit/No Credit
This course introduces the fundamental ideas and methods in causal inference, with examples drawn from education, economics, medicine, and digital marketing. Topics include potential outcomes, randomization, observational studies, matching, covariate adjustment, AIPW, heterogeneous treatment effects, instrumental variables, regression discontinuity, and synthetic controls. Prerequisites: STATS116/118, STATS 191/203. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Offered in Autumn 2025 at Stanford University.