Propensity Score Analysis
A 2-Day Seminar Taught by Shenyang Guo, Ph.D.
To view a sample of the course materials, click here.
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. The early registration fee of $895 is available until September 26.
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 $159 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 STSH25 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 25, 2018.
If you make reservations after the cut-off date, ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.
- 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 had learned PSM through Dr. Guo’s first edition of his book. Even after having read updates from the second edition, hearing and seeing his explanations substantially strengthened my grasp of PSM and matching estimators. I would highly recommend this course for anyone aiming to study causal relationships and/or conducting evaluation research.”
Christopher Campbell, Portland State University
“The instructor, Dr. Guo, does a good job covering the material, providing relevant examples with context, running (and walking you through) the data, reviewing the analyses, and relating it all back to the content. Answers questions as they arise while going through the material.”
Jennifer Sanchez, The University of Iowa
“An excellent overview of the theory of propensity score presented in a simple, easily accessible way. It takes someone with Dr. Guo’s knowledge and experience to be able to relay the information in such depth and simplicity. One of the best lecturers I’ve ever had!”
“This course was extremely helpful in providing a theoretical and hands-on introduction (i.e. annotated syntax and examples) to Propensity Score Analysis.”
Allison Williams, Happify Inc.
“This course was a helpful introduction to Propensity Score Analysis that provided great examples and detailed resources/recommended readings per course topic.”
Kristen Ward, University of Michigan
“The provided notebook of lecture slides, syntax, and example output is a truly invaluable source of information. I have now taken three of these courses, and I still regularly reference the notebooks at my job.”
John Merranko, University of Pittsburgh
“Dr. Guo is one of the top researchers in the field, and I learned a lot from him. His explanations and examples were very clear and straightforward, so now I understand PSA much better. Highly recommend to anyone conducting secondary data analysis!”
“Dr Guo covered classical and new approaches in PSA with hands-on data and syntax as well as the theoretical underpinnings. After this course, I feel confident in applying this method to my own research.”
Sangmi Kim, Augusta University, Georgia Cancer Center