Propensity Score Analysis
A 2-Day 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 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.
This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments.
This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments. To do the exercises, you will need to bring your own laptop computer with Stata (version 13 or higher) installed. In addition, you will need to download the R program here along with a package called optmatch. Power outlets will be provided at each seat
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 5 pm each day with a 1-hour lunch break from 12 pm to 1 pm 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 includes all course materials.
Lodging Reservation Instructions
A block of rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut St., Philadelphia, PA at a nightly rate of $152 for a Standard room. This hotel is about a 5-minute walk from the seminar location. To make a reservation, you must call 203-905-2100 during business hours andand use code STA331. For guaranteed rate and availability, you must make your reservation by March 1, 2016.
- 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
“This was a great opportunity to understand propensity score analysis and how it can be implemented using Stata in two days–the limitations as well as its applicability. Although not a student of statistics, I could comprehend the theory behind propensity analysis and also from where to gather more information. The credit for this goes entirely to Professor Guo.
Shyam Misra, National Institute of Allergy and Infection Disease of NIH
“Dr. Guo systematically introduced several propensity score analysis methods to us, along with useful Stata and R program packages, and reference articles. This course gives me a better understanding and I learned how to deal with causal inference problems. I would recommend this course to people in different fields; especially clinical and econometric fields.”
Menghan Chen, University of Pittsburgh
“This is an excellent course for those engaged in research exploring causality in empirical studies. I gained tremendous theoretical insights from Professor Guo’s illuminating analysis of a complex concept – PS Analysis.”
Madhu Mohanty, California State University
“I only heard about the name of “Propensity Score Analysis” before attending the course. Now I have a pretty good understanding of all the important methods involved in PSA. Dr. Guo is such a great instructor that he can explain complicated concepts in an easy way for us to understand. The hands-on programming instruction is invaluable. I feel pretty confident to conduct PSA on my own after the course. Thank you!
Ning Rosenthal, Premier Inc.