Treatment Effects Analysis

An Online Seminar Taught by
Stephen Vaisey, Ph.D.

Read reviews of the in-person version of this seminar

To see a sample of the course materials, click here.

To watch a sample video, click here.

This seminar covers both the theory and practice of Treatment Effects Analysis — a family of techniques that include parametric and non-parametric forms of matching and weighting observational data to enable better causal inferences. The most commonly known technique in this family is propensity score matching. We will cover this technique but you’ll soon see that propensity scores are just the tip of the iceberg; there are many, many more things you can do with your data to extract key insights.

The course takes place online in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 10 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.

This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 16 modules:

  1. The Potential Outcomes Framework
  2. The PO Framework, Continued
  3. Exact Matching
  4. Exact Matching and Effect Heterogeneity
  5. Propensity Scores
  6. Propensity Score Matching Example
  7. Propensity Score Matching in Stata
  8. Propensity Score Weighting and Covariate Balance
  9. Nearest-Neighbor Matching
  10. Nearest-Neighbor Matching in Stata 
  11. Nearest-Neighbor Matching, Continued
  12. Coarsened Exact Matching
  13. Loose Ends
  14. Regression Adjustment Models
  15. Doubly Robust Estimation
  16. Displaying Results and Advanced Topics

Each module consists of a video segment recorded from the live, 2-day version of the course. The modules contain all the lectures and slides from that course.

After each module there is a short multiple-choice quiz to test your knowledge. There are also four sets of exercises in which you can apply what you’ve learned to a real data set. An online discussion board is available for you to post questions or comments about any aspect of the course. All questions will be answered by Dr. Vaisey in a timely manner.   


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, doubly-robust estimators and entropy balancing. Participants will get extensive practical experience by working through case studies from economics, sociology, medicine, and public health.

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.


In the videos, Stata (Version 14 or higher) will be the main software package used to demonstrate the methods. Stata 13 can do at least 95% of what we will cover in this course. Some R will also be used to demonstrate techniques.

Participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy. 

WHO SHOULD sign up?

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.

reviews of the live version of Treatment Effects Analysis

“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 he course as it is the best course I have ever taken!”
  Michael Bowdish, University of Southern California

“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.”

“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