Causal Inference credit:

4 Hours

The statistical foundations and practical skills of causal inference are developed through principled study design and analysis of observational data. Students learn to distinguish causal claims from associational patterns by specifying assumptions, estimands, and identification strategies. Topics include potential outcomes, randomization inference, causal graphs, confounding, missing data, matching, propensity scores, regression adjustment, instrumental variables, sensitivity analysis, machine learning-assisted methods, treatment effect heterogeneity, and interference. Prerequisite: STAT 510 or STAT 511 , and STAT 527 ; or consent of instructor.