Propensity Score Analysis

3 units

Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating the effects of treatments or interventions when using nonexperimental or observational data. This PhD course focuses on three closely related, but technically distinct propensity score methods: (1) Propensity score matching and related methods, including greedy matching, optimal matching, propensity score subclassification, and propensity score weighting using Stata psmatch2, pweights and R optmatch; (2) Matching estimators using Stata nnmatch; and (3) Propensity score analysis with nonparametric regression using Stata psmatch2 and lowess. The examination of these methods will be guided by two conceptual frameworks: the Neyman-Rubin counterfactual framework and the Heckman scientific model of causality. The course also covers Heckman's sample selection model and Rosenbaum's approaches of sensitivity analysis to discern bias produced by hidden selections. The course uses Stata software to demonstrate the implementation of propensity score analysis. PhD students enrolled should be familiar with descriptive and inferential statistics. Students not meeting this prerequisite should contact the instructor to determine their eligibility to enroll in this course.