Treatment Effects Analysis
A 2-Day Seminar Taught by Stephen Vaisey, Ph.D.
This course offers an in-depth survey of a family of techniques known as treatment-effects estimators. Treatment-effects analysis is a quasi-experimental technique for estimating causal effects from observational data using the potential outcomes or counterfactual framework. These techniques — which include propensity-score matching, inverse probability weighting, and “doubly-robust” estimators — are now widely used in the social sciences, health sciences, and public policy.
The goal of treatment-effects analysis is to identify the causal effect of a treatment on an outcome, such as the effect of a college education on earnings, the effect of divorce on child outcomes, or the effect of a training program on employee productivity. A major advantage of treatment-effects techniques over standard regression methods is that they can produce different estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, an important distinction for policy work.
This seminar will take participants from simple exact matching to recent developments like coarsened exact matching and doubly-robust estimators. Participants will get extensive practical experience by working through case studies from economics, sociology, medicine, and public health.
Though the seminar will focus on hands-on understanding, we will also use causal graphs to look more deeply into the assumptions required to achieve unbiased estimates. Participant will learn to see how these techniques can be used in their own research.
We will cover a variety of topics including exact matching, propensity score matching and weighting, other forms of non-parametric matching and weighting, regression adjustment, and various forms of doubly-robust estimators. We will also consider tests for violations of assumptions and ways to test the sensitivity of results to violations of untestable assumptions. Although we will focus primarily on binary treatments, we will briefly explore how these techniques can be applied to multivalued treatments as well.
This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments.
The instructor will use Stata (and some R) to demonstrate the techniques. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with Stata installed. Stata 14 (any flavor) is preferred, though Stata 13 can do at least 95% of what we will cover in this course. Students do NOT need any prior knowledge of Stata to be able to complete the exercises.
Who should attend?
This course is for any who want to learn to apply this family of techniques to observational data. Participants should have a basic foundation in linear and logistic regression.
Location, Format, and Materials
The seminar meets Friday, December 9 and Saturday, December 10 at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
The class will meet from 9:00 to 5:00 each day with a one-hour break for lunch at noon.
Participants will receive a bound manual containing lecture notes, examples of computer code and results, and other useful information. This book frees participants from having to take notes during the seminar.
Registration and lodging
The fee of $995.00 includes all seminar materials. The early registration fee of $895.00 is available until November 9.
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 STA128 or click here. For guaranteed rate and availability, you must reserve your room no later than December 7, 2016.
If you make reservations after the cut-off date ask for the Statistical Horizon’s room rate (do not use the code) and they will try to accommodate your request.
1. The potential outcomes framework
a. The experimental ideal
b. Quasi-experiments and self-selection
c. Directed acyclic graphs (DAGs)
d. ATT: average treatment effect on the treated
e. ATU: average treatment effect on the untreated
f. ATE: average treatment effect
2. Exact matching
c. Requirements for estimating ATT/ATU/ATE
d. Other assumptions
3. Propensity score matching
a. P-scores as a solution to sparseness
b. Estimating the selection model
c. Matching on propensity scores
e. Testing overlap assumption
4. Propensity score weighting
a. “Missing data” motivation
b. Comparing weighting and matching
5. Non-parametric matching
a. Nearest-neighbor (Mahalanobis distance) matching
b. Coarsened exact matching
6. Regression adjustment
a. Balancing vs. conditioning
b. DAG motivation
7. Doubly-robust techniques
a. DAG motivation for DR techniques
b. Combining weighting and regression adjustment
8. Overview of advanced topics
a. Sensitivity analyses
b. Extensions to multivalued treatments
“As a biostatistician, this course is extremely useful and practical. The instructor explained clearly different methods and their advantages or limitations. Codes in STATA are very applicable and easily can be used in my own studies. Highly recommend this one to anyone trying to learn statistical matching.”
Jin Long, Stanford University
“The instructor is excellent. He explains everything very clearly. I learned a lot from this two-day seminar. It is really worth taking if you are doing something similar.”
Xuan Wang, Louisiana State University
“This course is a nice integration of a variety of statistical approaches and methods, few of which are covered in greater depth elsewhere. It’s a nice complement to courses on Causal Inference using DAGs, Instrumental Variables, and Propensity Scoring. Very helpful to the researcher trying to measure the effectiveness of government policies and other interventions.”
Mike Konrad, Software Engineering Institute of Carnegie Mellon University
“The instructor of this course did a great job in laying the foundation for the topic but also adapting the structure to address specific questions and examples. This presentation style was engaging and adaptable creating a positive space for questions.”
Emily Hennessy, Vanderbilt University
“Great overview of treatment effects. Very valuable as taught by Stephen Vaisey. He is open to listening to all questions and is very knowledgeable on the topic.”
Monica Munjal, Weill Cornell Medicine
“One of the most intellectually stimulating courses I’ve ever taken, full stop! Excellent instruction!”
Debbie Barrington, Georgetown University
“Found this course helpful in connecting the dots from previous courses of DAG’s Instrumental Variables and Propensity Scoring Analysis. Reinforcement and hands-on Stata helped answer many questions unanswered from previous courses.”
Robert Stoddard, Software Engineering Institute of Carnegie Mellon University
“Excellent course which covered new information on treatment effects analysis! Well organized and delivered.”
Hyekyun Rhee, University of Rochester