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. 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.
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.
SCHEDULE AND MATERIALS
The class will meet from 9 to 4 each day with a 1-hour lunch break.
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 $895 includes all course materials.
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $142 per night for a Standard room. This location is about a 5 minute walk to the seminar location. In order to make a reservation, call 203-905-2100 during business hours and identify yourself by using group code STA116. The room block will expire when it is full or on October 6, 2014.
- 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 think the best part of this course is that you get to know the breadth of the methods under the umbrella of Propensity Score Analysis. Choosing what method fits your research objective is a decision that can be made based on the usefulness of the findings.”
Vishnu Nepal, Houston Department of Health
“As a health care researcher, I appreciated the comprehensive and time-effective course on propensity score analysis. Complicated methods were made simple and put into context.”
Martin Almquist, Lunds University, Sweden
“This is an excellent program to quickly go through some important methods. The breadth of material covered is very good.”
Shiva Agarwal, University of Pennsylvania
“The seminar was very helpful in deepening my knowledge of propensity score analysis. Guo is a very good teacher and all the materials provided in the seminar were so understandable!”
Soyoon Weon, McGill University