Matching and Weighting for Causal Inference
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 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.
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 Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
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 Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $154 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH1107 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, October 7, 2019.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.
1. Theoretical background
a. The experimental ideal
b. The potential outcomes framework
c. Directed acyclic graphs
d. Defining different treatment effects
e. Conditional independence assumption
2. Exact matching
a. The idea of exact matching
b. Implementation in MatchIt
c. Getting treatment effects with WLS
3. Propensity-score methods
b. The traditional matching workflow
c. Propensity score estimation
– logistic regression (MatchIt)
– generalized boosted models (twang)
– covariate balancing propensity scores (CBPS)
d. Uses for propensity scores
e. Balance and standardized bias (twang and cobalt)
4. Non-parametric methods
a. Mahalanobis distance matching (MatchIt/Matching)
b. Genetic matching (MatchIt/Matching)
c. Entropy balancing (ebal)
d. Coarsened exact matching (cem/MatchIt)
5. Parametric regression with preprocessed data
a. Double robustness property
b. Standard errors
6. Advanced topics (as time permits)
a. Sensitivity analysis
b. Multilevel applications
c. Panel applications
“Great materials! Highly recommend for anyone who wishes to be up to speed on modern techniques in causal inference in observational research.”
Oksana Pugach, University of Illinois at Chicago
“This course was terrific. Stephen was able to explain the theory, techniques, and application related to almost all approaches to matching and weighting. I’ve taken several post-doctoral stats courses and this is one of the best I have experienced. Stephen is also very flexible and accommodating for participants’ interests and skill level.”
Christopher Campbell, Portland State University
“I had a background in R, but not with this material. The material was presented in a straightforward manner. Steve was clear, knowledgeable, and engaging. The accompanying materials were great and included sample R code. I feel pretty confident in applying these techniques to our future research projects. It’s ‘magic’!”
Paul Kuo, University of South Florida
“Dr. Vaisey is a gifted teacher! His course provided an overview of the field while also providing practical skills and critical appraisal of each method. I highly recommend this course.”
Catherine Crespi Chun, University of California, Los Angeles