Wednesday February 28, 12:00 - 3:00 PM
Workshop D
New Matching Methods for Causal Inference

Jose R. Zubizarreta, Harvard University

In observational studies of causal effects, matching methods are extensively used to approximate the ideal study that would be conducted if controlled experimentation was possible. In this workshop, we will explore new advancements in matching methods that overcome three limitations of standard matching approaches, and we will:
(1) directly obtain flexible forms of covariate balance, ranging from mean balance to balance of entire joint distributions, (2) produce self-weighting matched samples that are representative of target populations by design, and (3) handle multiple treatment doses without resorting to a generalization of the propensity score, instead balancing the original covariates. We will discuss extensions to matching with instrumental variables, in discontinuity designs, and for matching before randomization in experiments.
The methods discussed build upon recent advancements in computation and optimization for big data. We will use the statistical software package 'designmatch' for R.

Participants will gain a clear picture of role of matching for causal inferences, and its pros and cons. They will learn how to construct balanced and representative matched samples, improving on each aspect in relation to traditional matching methods on the estimated propensity score. The target audience of the workshop is applied researchers with quantitative training and familiarity with traditional regression methods. Facility with R is ideal, but not strictly necessary as well-documented step-by-step code will be provided.

Show all professional development options?