Propensity Score Analysis

Taught by Shenyang Guo, Ph.D. 

Read reviews of this seminar

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.


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 from 12 pm to 1 pm at The Conference Center, One North Wacker Drive, Chicago, Illinois 60606.

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 seminar materials. 

Lodging Reservation Instructions

A block of guest rooms has been reserved at La Quinta, One S. Franklin, Chicago, IL 60606 at a special rate of $189 per night. To make reservations, call 866-527-1498 during business hours and refer to the Statistical Horizons group. Availability is limited. Please make your reservation as soon as possible and before the rate expires on Tuesday, April 28, 2015.

Seminar outline

  1. Observational studies and outlines
  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


“This is an amazing course. No one could did it better! Professor Guo is thorough in going through all propensity methods, discussing strengths and weaknesses of each. In addition, the written materials are excellent and include actual Stata code (with output!). I am now ready to dive all the way into my dissertation analysis and make a great leap forward.”
  Wendy Wiegmann, University of California, Berkeley 

“This course was excellent in conveying complex conceptual information about the theories and practical application of propensity scoring methods in a clear and practically accessible manner.”
  Jodi Edwards, Sunnybrook Health Sciences Centre

“Professor Guo is an excellent presenter.”
  Ibrahim Al Zakwani, Sultan Qaboos University Hospital

“I have not taken a course on this topic before and needed it in order to complete a research paper. I now feel confident to do my analyses. The key is the integrated overview with many worked out examples and sample code.”
  Lenny Lopez, Massachusetts General Hospital