Causal Inference

Graduate / Professional

This course provides an intensive introduction to the statistical theory and practice of causal inference. Topics include: the potential outcomes framework, experimental design, selection on observables, instrumental variables, difference-in-differences, and panel data. Prerequisite: PLSC 500 or equivalent. Students are expected to come in with prior knowledge about statistical inference, regression/linear models, and statistical computing in R. HTBA