Matching and Weighting for Causal Inference
with R

A 2-Day Seminar Taught by Stephen Vaisey, Ph.D.

Read reviews from other seminars taught by Stephen Vaisey


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.

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 with at least one hour each day devoted to carefully structured and supervised assignments.


COMPUTING

The instructor will use R with RStudio to demonstrate the techniques. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with R and RStudio 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.


LOCATION, Format, and MATERIALS

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at the Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611.

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 includes all course materials.

Refund Policy

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 Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611, where the seminar takes place, at a special rate of $229. In order to make reservations, click here. For guaranteed rate and availability, you must reserve your room no later than Monday, May 6, 2019.

We also recommend going directly to the hotel’s website or checking other online hotel sites. Pricing varies and you may be able to secure a better rate. 


SEMINAR OUTLINE

1. The potential outcomes framework
     a. The experimental ideal
     b. Quasi-experiments and self-selection
     c. Directed acyclic graphs (DAGs)
     d. Types of treatment effects (e.g., ATE/ATT)
2. Exact matching
     a. Stratification
     b. Weighting
     c. Requirements for estimating ATT/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
     d. Calipers
     e. Testing overlap
4. Propensity score weighting
     a. “Missing data” motivation
     b. Inverse probability weighting for different estimands
     c. Comparing weighting and matching
5. Non-parametric matching
     a. Nearest-neighbor (Mahalanobis distance) matching
     b. Matching frontier
6. Non-parametric weighting
     a. Coarsened exact matching
     b. Entropy balancing
7. Doubly-robust estimation
     a. DAG motivation for double robustness
     b. Combining matching and weighting with regression
8. Advanced topics
     a. Sensitivity analyses
     b. Extensions to multivalued treatments
     c. What’s next?


RECENT COMMENTS FROM PARTICIPANTS

“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

“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

“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

“Steve is the professor I wish I’d had in graduate school. He is a black belt at theory and technical details, and has the ability to communicate the materials in a way that helps you to grow an intuition. This is a rare quality in a statistician and teacher, and Steve nails it. He exhibits humor, thoughtful questions and responses, and the ability to anticipate where people get “stuck.” Take a course from Steve and you’ll be glad you did it!”
  Andy Bogart, RAND Corporation

“Stephen did an excellent job making difficult concepts easy to understand through examples and clear explanations. I learned how to better interpret, compare, and create practical models, all of which apply to many research projects with which I am involved.”
  Scott Friedlander, Los Angeles Biomedical Research Institute