Propensity Score Analysis
A 3-Day Remote Seminar Taught by Shenyang Guo, Ph.D.
To view a sample of the course materials, click here.
This seminar is currently sold out. Email firstname.lastname@example.org to be added to the waitlist.
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.
Starting September 10, we are offering this seminar as a 3-day synchronous*, remote workshop for the first time. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time. An additional session will be held Thursday and Friday afternoons as an “office hour”, where participants can ask any questions.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
MORE DETAILS ABOUT THE COURSE CONTENT
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 Register?
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.
2. Conceptual Frameworks and Assumptions
3. Propensity Score Matching (I)
4. Propensity Score Matching (II)
5. Matching Estimators
6. Kernel-based Matching Estimator
7. Rosenbaum’s Sensitivity Analysis
8. Debates and Directions of Future Development
“Dr. Guo did an excellent job communicating complicated material in a way that was easily understandable and applicable to our real-world everyday work. Thank you for a great course.”
Andrew Brown, Ottawa County Recovery Court
“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
“Extremely helpful! The professor is great at explaining complex concepts and making the discussion very interesting.”
Carmen Capo-Lugo, University of Alabama at Birmingham
“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
“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.