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.

Although regression analysis is most often used to adjust for potentially confounding variables, propensity score analysis is an attractive alternative. Results produced by propensity score methods are typically easier to communicate to lay audiences. And propensity score estimates are often more robust to differences in the distributions of the confounding variables across the groups being compared.

This seminar will focus on four closely related but technically distinct propensity score methods: 1) Propensity score matching and related methods, including greedy matching, optimal matching, and propensity score weighting using Stata psmatch2, pweights and R optmatch; 2) Matching estimators using Stata nnmatch; 3) Propensity score analysis with nonparametric regression using Stata psmatch2 and lowess; 4) Instrumental variable approach, Heckman’s sample selection model. 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 Rosenbaum’s approaches of sensitivity analysis to discern bias produced by hidden selections.

The seminar uses Stata software to demonstrate the implementation of propensity score analysis. Although participants will not do hands-on work during the seminar, they are encouraged to practice on their own time. All syntax files and illustrative data can be downloaded at the Propensity Score Analysis support site https://ssw.unc.edu/psa.