Matching and Weighting for Causal Inference
A 4-Day Remote Seminar Taught by Stephen Vaisey, Ph.D.
To see a sample of the course materials, click here.
This seminar is currently sold out. Email email@example.com to be added to the waitlist.
This course offers an in-depth introduction to matching and weighting methods using the R package. Matching and weighting are quasi-experimental techniques for estimating causal effects from observational data using the potential outcomes or counterfactual framework. They are often (but not always) based on propensity scores. These techniques are now widely used in the social sciences, health sciences, management and public policy.
Starting July 7, we are offering this seminar as a 4-day synchronous*, remote workshop for the first time. Each day will consist of a 3-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they 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 one’s own that afternoon. A final session will be held each evening as an “office hour”, where participants 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, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.
MORE DETAILS ABOUT THE COURSE CONTENT
Researchers use matching and weighting 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 — when assignment to the treatment is not random. A major advantage of these techniques over standard regression methods is that they can easily produce different estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, a distinction that is especially important for policy work.
This seminar will guide participants from simple exact matching to recent developments like coarsened exact matching, entropy balancing, and matching frontier techniques that show how effects vary across the full range of possible match quality. We will also show how to integrate matching with regression to create “doubly robust” estimates of causal effects. Participants will get practical experience by working through exercises from the social and health sciences.
Though the seminar will focus on hands-on understanding, we will also use causal graphs (directed acyclic graphs or DAGs) to look more deeply into the assumptions required to achieve unbiased estimates. Participants will learn how these graphs can be used in their own research.
This is a hands-on course.
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Prior to each session, participants will receive an email with the meeting code you must use to join.
The instructor will use R with RStudio to demonstrate the techniques. To participate in the hands-on exercises, you are strongly encouraged to have your computer available with R and RStudio already installed. Students need only a basic familiarity with R to get the full value of the course.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
WHO SHOULD ATTEND?
This course is for any who want to learn to apply matching and weighting to observational data to improve their causal inferences. Participants should have a basic foundation in linear and logistic regression.
1. Theoretical background
a. the experimental ideal
b. the potential outcomes framework
c. defining different treatment effects
d. conditional independence assumption
2. Exact Matching
a. the goal of exact matching
b. implementation in MatchIt
c. getting treatment effect estimates with WLS
d. feasible estimates
3. Propensity-score methods
a. theory and estimation
b. propensity-score stratification in MatchIt
c. propensity score matching in MatchIt
d. assessing covariate balance
– mean differences
– Kolmogorov-Smirnov distances
– using cobalt for plotting balance diagnostics
e. matching variations: replacement, calipers, etc.
f. bootstrapping for standard errors
g. propensity-score weighting
– IPTW using WeightIt
– covariate balancing propensity scores using WeightIt
4. Non-parametric methods
a. Mahalanobis distance matching in MatchIt
b. entropy balancing in WeightIt
5. Parametric regression with preprocessed data
a. double robustness property
b. estimation with MatchIt/WeightIt + lm
6. Advanced topics (briefly as time permits)
a. multinomial treatments in WeightIt
b. continuous treatments in WeightIt
c. synthetic control method (difference-in-differences + weighting)
“This course was superb for getting a feel of what propensity models are and how to use them across many different applications. There are a lot of variations covered and the caveats of all are thoroughly discussed. Steve is a fantastic, enthusiastic, and engaging speaker. I would definitely recommend.”
Emily Bucholz, Boston Children’s Hospital
“Having worked in various research capacities following my Ph.D. training, this course provided the most robust instruction on causal inference and matching/weighting methodology that I’ve experienced to date. Dr. Vaisey provides practical applications for both parametric and non-parametric techniques, all supported by well thought out code and visualizations. I unequivocally recommend this course to anyone researching or working in a field involving causal inference analyses or applications.”
Lauren Johns, Independence Blue Cross
“This was a very interesting course that rapidly takes you from the basics to the cutting edge of this family of techniques.”
Neel Butala, Massachusetts General Hospital
“Stephen is an excellent teacher, and this course greatly helped fill in some fundamental gaps in my understanding. I feel like I have a much stronger foundation for causal inference research after completing this course.”
Justin Williams, University of California, Los Angeles
“Dr. Vaisey is one of the best teachers I’ve had the pleasure encountering. His ability to clearly articulate deep, complex concepts in ways that ‘click’, through nimble analogies and quick-witted humor, is second to none. His ability to think on the fly and answer questions effectively, and in an entertaining way, is superb. When his explanation of the dubious foundation for rules of thumb led to ‘It’s turtles all the way down…’, I nearly lost it. I highly recommend this class for intellectually curious researchers looking to understand and enjoy a two-day journey through state-of-the-art causal inference.”
Darren Stewart, United Network for Organ Sharing