Propensity Score Analysis: Basics – October 2024
Event Phone: 1-610-715-0115
Upcoming Dates
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03OctPropensity Score Analysis: Basics10:00 AM-3:30 PM
Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.
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. Most important, in PSA we model the assignment to treatment without knowledge of the outcomes, thereby ensuring the objectivity of the design.
This seminar will focus on basics of running propensity score analysis. By taking this seminar, you will learn how to use logistic regression and generalized boosted regression to estimate propensity scores, and how to use the estimated propensity scores to run propensity score matching and related models.
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.
Venue: Livestream Seminar