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.   


background

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.


computing

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

“Dr. Vaisey is a great teacher who is able to impart a significant amount of insight and understanding in a short period of time. Great energy and enthusiasm, very clear. Thanks so much. What I’ve learned is very valuable.”
  Kenneth Coburn, Health Quality Partners

“This course was extremely helpful. I received a great overview of common techniques used to estimate treatment effects and the foundational knowledge I will need for further learning on this topic. The instructor was great.”
  Christina Andrew, University of South Carolina

“This course gives a thorough appreciation of the benefits and disadvantages of different matching models. For those of us still hanging onto techniques learned in grad school many moons ago, it was eye-opening to see where the field had progressed. I enjoyed seeing the application of different methods rather than just the theoryIt was also beneficial that the focus was on the intuition behind the different methods. I would definitely recommend to anyone interested in learning about matching methods.”
  Anonymous

“The instructor had excellent mastery of the topic and yet was able to translate his knowledge with great clarity to those new to the concepts. I appreciated his consistent employment of real-world examples to help solidify my understanding of a technique’s applications.”
  Emily Hawks, Adobe Systems

“This course is an excellent introduction to advanced techniques used for treatment effects analysis. I would recommend the course to those that are new to the subject area. Dr. Vaisey’s enthusiasm and experience made the course well worth the investment in time out of a busy work schedule.”
  Tim Hediger, Doylestown Hospital 

“I enjoyed the course on treatment effects analysis by Dr. Stephen Vaisey. I’m a doctoral candidate working on my dissertation. This course helped me understand propensity score matching and other matching strategies that I can use for my dissertation research. I’m looking forward to applying what I’ve learned to my dissertation research project.”
  Zibei Chen, Louisiana State University

“The course explains in depth various treatment analysis methods available and the improvement made on these methods over the past 50 years. These methods or concepts can be used in a variety of fields, not specific to research.”
  Karthikeyan Moorthy, Adobe Systems

“One of the best statistical lectures I have ever taken!”
  Anonymous

“Stephen Vaisey is a remarkable instructor. His command of the subject is outstanding and his ability to communicate the course content is impressive. He uses numerous examples and takes various approaches to explain concepts through the seminar. Such intense introductions have a tendency to feel long and tiring, so I was pleasantly surprised to find that this seminar was often fun and surprisingly engaging!”
  Andrew Dierkes, University of Pennsylvania