Propensity Score Analysis

A 2-Day Seminar Taught by Shenyang Guo, Ph.D. 

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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.


LOCATION, FORMAT, MATERIALS.

The seminar meets Friday, October 4 and Saturday, October 5 at the Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036. 

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 rooms has been reserved at the Courtyard Washington Embassy Row,1600 Rhode Island Avenue, NW, Washington, DC  20036 at a special rate of $159 per night.  In order to guarantee rate and availability, make your reservations by calling Marriott at 1-888-236-2427 or 1-202-448-8004 no later than Wednesday, September 4 and identify yourself with Statistical Horizons. 


Seminar outline

  1. Observational studies and challenges
  2. Why and when propensity score analysis is needed
  3. The Neyman-Rubin counterfactual framework
  4. The assumption of strongly ignorable treatment assignment
  5. The stable unit treatment value assumption
  6. Truncation, censoring, and incidental truncation
  7. The Rosenbaum and Rubin model (1983)
  8. Greedy matching; Stata-psmatch2
  9. Optimal matching; R-optmatch
  10. Post-optimal-matching analysis
  11. Propensity score weighting
  12. Matching estimators; Stata-nnmatch
  13. The kernel-based matching estimator; Stata-psmatch2
  14. Rosenbaum’s sensitivity analysis; Stata-rbounds
  15. Overview of Heckman’s model of causality 
  16. Criticism of nonexperimental approaches
  17. Directions of future development 

RECENT COMMENTS FROM PARTICIPANTS

“This course was an excellent explanation of the theoretical concepts & Stata scripts. This will help me correctly & confidently analyze my dissertation data.”
        Jennifer Roark, University of Colorado 

“A very compact and intense introduction to propensity score models. The material was well balanced, and the instructor was able to present complex methods in clear and applied ways.”
        Javier Gimeno, INSEAD

“The course is a good balance between theory and application. Examples were given in order to demonstrate the different propensity score related methods and explanation for applying in real world examples.” 
        Tom Weichle, VA Information Resource Center 

“The course on Propensity Score Analysis was comprehensive and well organized. Dr. Guo is an excellent teacher – patient, clear, concise and easy to follow. Plus he is a genuinely nice person! We covered a lot of ground. It was well worth the time.”
         Kathryn Kost, Guttmacher Institute