STATS209A

This course introduces the fundamental ideas and methods in causal inference, and surveys a broad range of problems and applications. Emphasis will be on framing causal problems and identifying causal effects in both randomized experiments and observational studies. Topics will include: the potential outcomes framework; randomization-based inference and covariate adjustment; matching, and IPW; instrumental variables, regression discontinuity and synthetic controls. Examples and applications will be taken from the fields of education, political science, economics, public health and digital marketing.