Treatment Effects Analysis
A 2-Day 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.
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.
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. Participants 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) 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 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 class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Jamaica Bay Inn, 4175 Admiralty Way, Marina Del Rey, CA 90292.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995.00 includes all seminar materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Jamaica Bay Inn, 4175 Admiralty Way, Marina Del Rey, CA 90292, where the seminar takes place, at a special rate of $209-$229 per night. In order to make reservations, call 310-823-5333 during business hours and identify yourself as part of the Statistical Horizons group. For guaranteed rate and availability, you must reserve your room no later than Monday, April 16, 2018.
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
“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, Healthy 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 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 theory. It 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.”
“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!”
“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