Stat 256: Causality
Instructor: Chad Hazlett (chazlett@ucla.edu) Quarter: Spring 2026 Class: Monday, Wednesday, 11:00–12:15, Haines A44 Department: Statistics, UCLA
This course covers causality as it applies to the challenges of real applied research, with fluency in both the potential outcomes and structural causal model / DAG frameworks. Topics include identification strategies (experiments, selection on observables, difference-in-differences, instrumental variables, regression discontinuity), sensitivity analysis and partial identification, and advanced topics such as mediation, interference, and time-varying treatments.