Propensity Score Analysis: A Second Course
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.
To reflect the most recent advances in the field, this Second Course of Propensity Score Analysis reviews the statistical principles and applications of important models recently developed. It illustrates how these models can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the course explores various strategies for employing Propensity Score Analysis, discusses the use of Propensity Score Analysis with alternative types of data, and delineates the limitations of Propensity Score Analysis under a variety of constraints. This course will focus on the following topics:
- Overview of the origins, history, and statistical foundations of Propensity Score Analysis
- Handling multilevel data in estimating and using the propensity scores
- Addressing the issue of limited overlap of estimated propensity scores between treatment and control groups
- Conducting propensity score subclassifications
- Newly developed methods for analyzing treatment dosage with generalized propensity scores
- Tests of heterogeneous treatment effects and modeling issues
- Running Propensity Score Analysis in conjunction with structural equation modeling
The seminar uses Stata software to demonstrate the implementation of propensity score analysis. Most programs are ado files developed by the original authors of the newly developed models. 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. Taking the first course of Propensity Score Analysis is not a prerequisite.
LOCATION, FORMAT, MATERIALS
The seminar meets Friday, November 4 and Saturday, November 5 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. The class will meet from 9 to 4 each day with a 1-hour lunch break.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), all data sets used in the workshop, as well as examples of computer output 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.
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 $149 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 STA003 or click here. For guaranteed rate and availability, you must reserve your room no later than October 2, 2016.
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.
Morning of Day 1
– Overview of Propensity Score Analysis
– Tests of heterogeneous treatment effects
Afternoon of Day 1
– Handling multilevel data using propensity scores
– Addressing the problem of limited overlap of estimated propensity scores
Morning of Day 2
– Propensity score subclassification
– Running structural equation modeling in conjunction with Propensity Score Analysis
Afternoon of Day 2
– Analyzing treatment dosage with generalized propensity scores
“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