Causal Inference without Balance Checking: Coarsened Exact Matching

Causal Inference Without Balance Checking: Coarsened Exact Matching

We discuss a method for improving causal inferences called ‘‘Coarsened Exact Matching’’ (CEM), and the new ‘‘Monotonic Imbalance Bounding’’ (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a...

cem: Software for Coarsened Exact Matching

This program is designed to improve causal inference via a method of matching that is widely applicable in observational data and easy to understand and use (if you understand how to draw a histogram, you will understand this method). The program implements the coarsened exact matching (CEM) algorithm, described below. CEM may be used alone or in combination with any existing matching method. T...

cem: Coarsened Exact Matching in Stata

This paper introduces a Stata implementation of Coarsened Exact Matching (CEM), a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. CEM is faster, easier to use and understand, requires fewer assumptions, more easily automated, and possesses more attractive statistical properties for many applications than existing ...

Matching Methods for Causal Inference

Bayesian Matching for Causal Inference

In this paper we provide Bayesian matching methods for finding the causal effect of a binary intake variable x ∈ {0, 1} on an outcome of interest y. One technique we introduce is a Bayesian variant of the classic Rosenbaum and Rubin (1983, 1984) propensity score matching method. We show how it is possible to find the posterior distribution of the Bayesian matched sample average treatment effect...