Topics in Causal Inference

Graduate / Professional

This course explores modern topics in causal inference with a focus on randomized experiments and experimental design. The course develops the potential outcomes framework and builds toward advanced topics including covariate balancing, overlap conditions and positivity violations, causal inference under interference, adaptive experimentation, and policy evaluation. The emphasis is on both theoretical foundations and applied practice. Causal inference from observational data is de-emphasized. Students complete presentation and project work including both applied and theoretical replication studies. Prerequisite: Ph.D. standing or permission from instructor. W 9:25am-11:20am