Matching and Weighting for Causal Inference with R - Online Course

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 you 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. You 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. You will learn how these graphs can be used in your own research.

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.

Basic familiarity with R is highly desirable. If you are new to R, check out Professor Vaisey’s one-hour Introduction to R video to get up to speed. You may want to consider taking a short introductory seminar on R. But even novice R coders will be able to follow the lectures and do the exercises.

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.

This course is for anyone who wants to learn to apply matching and weighting to observational data to improve their causal inferences. You should have a basic foundation in linear and logistic regression.

1. Theoretical background
   1. The experimental ideal
   2. The potential outcomes framework
   3. Defining different treatment effects
   4. Conditional independence assumption
2. Exact matching
   1. The goal of exact matching
   2. Implementation in MatchIt
   3. Getting treatment effect estimates with WLS
   4. Feasible estimates
3. Propensity-score methods
   1. Theory and estimation
   2. Propensity-score stratification in MatchIt
   3. Propensity-score matching in MatchIt
   4. Assessing covariate balance
      • Mean differences
      • Kolmogorov-Smirnov distances
      • Using cobalt for plotting balance diagnostics
   5. Matching variations: replacement, calipers, etc.
   6. Bootstrapping for standard errors
   7. Propensity-score weighting
      • IPTW using WeightIt
      • Covariate balancing propensity scores using WeightIt
4. Non-parametric methods
   1. Mahalanobis distance matching in MatchIt
   2. Entropy balancing in WeightIt
5. Parametric regression with preprocessed data
   1. Double robustness property
   2. Estimation with MatchIt/WeightIt + lm
6. Advanced topics (briefly as time permits)
   1. Multinomial treatments in WeightIt
   2. Continuous treatments in WeightIt
   3. Synthetic control method (difference-in-differences + weighting)

The fee of $995 includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.