Propensity Score Analysis
A 2-Day Seminar Taught by Shenyang Guo, Ph.D.
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 from 12 pm to 1 pm 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 includes all course materials. The early registration fee of $895 is available until March 7.
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 $154 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 STA406 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 06, 2017.
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
“Professor Guo delivered an excellent two-day workshop providing cutting-edge knowledge in PSM. Without too much formulae-heavy slides he is able to explain the theory in an intuitive way without a loss of rigor. Professor Guo brought in his previous experience in using PSM to the class which makes many topics particularly useful and practical.”
Yang Yang, Temple University
“I decided to take Propensity Score Matching when I decided to use the method in data analysis. I was relieved to get code, examples, and ideas for displaying my data. The course makes me feel more comfortable evaluating the limitations of my analysis and ways to optimize it!”
Hayley Germack, University of Pennsylvania School of Nursing
“The course was great in that whether you had R and Stata installed or not, the learning of tool usage still occurred.”
Robert Stoddard, Carnegie Melon University
“This class gave a great review of and introduction to those methods. Thank you!”
CHERP, Crescenz/Philadelphia VA Medical Center
“Great teacher and great materials in order to apply material to my own PSM needs. The Stata and R files (and files, data, etc.) are invaluable!”
“After taking this course, I feel ready to apply the methods to my own data. Although the two-day course could not cover all aspects of propensity score analysis, Dr. Guo’s presentation, in combination with the course book/notes, exercises and website have provided me with the resources to apply the methods covered in the course to my own data and the foundation to build my capacity in propensity score matching.”
Danielle Naugle, University of Pennsylvania