Treatment Effects Analysis
A 3-Day Remote Seminar Taught by
Stephen Vaisey, Ph.D.
To see a sample of the course materials, click here.
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.
Starting November 12, 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. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where you can review the exercise results with the instructor and 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 and will be accessible for one week after the seminar, 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 take you from simple exact matching to recent developments like coarsened exact matching, doubly-robust estimators and entropy balancing. You 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. You will learn to see how these techniques can be used in your 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 remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Before the seminar begins, you will receive an email with the meeting code and password you must use to join.
The instructor will use Stata (and some R) to demonstrate the techniques. To participate in the hands-on exercises, you are strongly encouraged to use a computer with Stata installed. Stata 14 (any flavor) or above 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 Register?
This course is for any who want to learn to apply this family of techniques to observational data. You should have a basic foundation in linear and logistic regression.
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
“I’ve been confounded by studying propensity score matching on my own – there are many methods to apply, and thick books and articles, which is discouraging. During this 2-day course, I’ve gained a greater understanding and more confidence about applying treatment effects analysis to the observational data I analyze. Steve, the instructor, makes the methods, programs, and interpretation of results very clear. He is a gifted teacher!”
Philip Ituarte, City of Hope
“This course is a great way to learn or enhance your knowledge about treatment effects. Steve did a great job in explaining the basics and incorporating the most updated research in this area.”
“Steve is a fantastic teacher. The material is very well-presented, clear, yet concise. Steve presents the concepts of matching and creates balance among treatment groups in a way that is immediately applicable to your own research! I highly recommend the course as it is the best course I have ever taken!”
Michael Bowdish, University of Southern California
“This course not only introduced me to a new way of approaching analysis of observational studies, but clearly explained rationale. The instructor was clear and provided good examples ranging from simple to more complex.”
Colleen Azen, Children’s Hospital Los Angeles
“I’ve taken this associated material from Alberto Abadie, Jens Hainmueller, and Don Rubin (all big hitters in this subject). I wanted a hands-on, applied refresher of this material. I liked Steve’s approach and found it to be really accessible, like all of my experiences with your training. In fact, I wish I had taken Steve’s class before the instructors listed above. Theory and equations are always nice and have their place, but most clinicians want to know how to properly implement a procedure the next day in the workplace. Believe me, I understand the challenge to balance the technical with the applied!”
Matthew Jones, Walden University