Propensity Score Analysis

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

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To view a sample of the course materials, click here.

This course is currently full. If you would like to be added to the waitlist, please send us an email at ashley@statisticalhorizons.com.


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.

Refund Policy

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

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $164 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH1003 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 3, 2019.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.


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

RECENT COMMENTS FROM PARTICIPANTS

“Professor Guo explained concepts clearly and he is an amazing teacher! He beautifully articulated the challenging concept in an easy way. What I really appreciated about this course is Dr. Guo’s deep knowledge on propensity score analysis. He effectively used his project as an example and this was very helpful to think about how to analyze and present the data. Dr. Guo’s course is effectively organized and encourages students to think critically about the methods. He is very passionate and kind and frequently cites great articles!”
  Jane Lee, Boston University

“Dr. Guo aided in making complex material more accessible than I have experienced in other statistical training. He offered clear examples of how to apply the instructional material, struck a helpful balance between conceptual/theoretical instruction and applied demonstrations, and was open and receptive to all student questions. I’d highly recommend this course for individuals who are seeking an introduction to propensity analysis.”
  Erin Reilly, University of California, San Diego

“This course is excellent. Professor Guo makes sure to cover the most recent/relevant techniques. There are several practical examples. Definitely an extremely useful course for anybody who would like to conduct some impact evaluation. A suggestion: buy and read his book before taking the course. I recommend this course without any reservation.”
  Slim Haddad, Université Laval 

“The seminar helped me understand different approaches for propensity score methods and when to use which approaches.”
  Anonymous

“Definitely check out this course if you want to know more about propensity score analysis apart from logit regression and nearest neighbor matching!”
  Siyuan Shen, University of Pennsylvania

“The course material was presented in a clear manner. A good overview of methods illustrated with examples. I enjoyed the course and got a lot out of it! The instructor was very knowledgeable, clear, and enthusiastic.”
  Ellen Snyder, Merck & Co., Inc.