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A 3-Day Livestream Seminar Taught by Shenyang Guo, Ph.D.

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 two methods to estimate propensity scores, and four methods to run corrective models of outcome analysis to enhance the study’s internal validity:

• Using logistic regression and generalized boosted regression to estimate the propensity scores.
• The classic matching methods, including nearest neighbor within caliper matching and Mahalanobis metric matching.
• The optimal matching methods.
• The inverse probability of treatment weights estimator, also known as propensity score weighting method.
• The Abadie and Imbens’s matching estimators.

The examination of these methods will be guided by the Neyman-Rubin counterfactual framework.