Propensity Score Analysis
A 2-Day Seminar Taught by Shenyang Guo, Ph.D.
To view a sample of the course materials, click here.
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 three closely related but technically distinct propensity score methods:
- Propensity score matching and related methods, including greedy matching, optimal matching, and propensity score weighting using Stata psmatch2, pweights and R optmatch
- Matching estimators using Stata nnmatch
- 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 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. All syntax files and illustrative data can be downloaded at the Propensity Score Analysis Support Site.
Who should attend
The seminar will be helpful to researchers who are engaged in intervention research, program evaluation, or more generally causal inference, when their data were not generated by a randomized clinical trial. The prerequisite for taking this seminar is knowledge of multiple regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines may consider participating.
LOCATION, Format, and MATERIALS
The class will meet from 9 am to 4 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995.00 includes all seminar materials. The early registration fee of $895 is available until March 5.
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).
Lodging Reservation Instructions
Hotel information will be posted when available.
- Observational studies and outlines
- Why and when propensity score analysis is needed
- The Neyman-Rubin counterfactual framework
- The assumption of strongly ignorable treatment assignment
- The stable unit treatment value assumption
- Truncation, censoring, and incidental truncation
- The Rosenbaum and Rubin model (1983)
- Greedy matching; Stata-psmatch2
- Optimal matching; R-optmatch
- Post-optimal-matching analysis
- Propensity score weighting
- Matching estimators; Stata-nnmatch
- The kernel-based matching estimator; Stata-psmatch2
- Rosenbaum’s sensitivity analysis; Stata-rbounds
- Overview of Heckman’s model of causality
- Criticism of nonexperimental approaches
- Directions of future development
“I highly recommend the Propensity Score Analysis course with Dr. Shenyang Guo. I greatly enjoyed this course and learned a lot. He is my favorite statistics teacher from all the courses I have taken.”
Yu Lu, University of Texas Medical Branch
“This course covers an amazing array of topics in just 14 hours! The instructor, Professor Guo, explains course material extremely clearly and offers insights from an expert researcher’s perspective which one may not be able to get just by reading books. I highly recommend this course to all including professional statisticians or even an academic researcher who is not familiar with causal inference.”
Jinbo Chen, University of Pennsylvania
“Dr. Guo is amazing. Very passionate on the subject and an authority! Clear in stating the problem and solution. A great class!”
“I liked being able to get knowledge I can apply to my research immediately upon leaving the course. This workshop covers material not included in my graduate curriculum.”
Alexandra Houston-Ludlam, Washington University in St. Louis