Causal Inference without Balance Checking: Coarsened Exact Matching

TLDR

Coarsened Exact Matching (CEM) is a method for improving causal inference, derived from the Monotonic Imbalance Bounding (MIB) class of matching methods. The paper aims to enhance causal inference by developing and extending CEM, deriving new statistical properties, and linking these theoretical advances to practical applications. The authors derive and illustrate new statistical properties of CEM, propose extensions, and provide open‑source software for R, Stata, and SPSS to implement these methods. CEM offers a broad set of statistical properties unavailable in most other matching methods while remaining easy to understand and use.

Abstract

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 wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R, Stata , and SPSS that implement all our suggestions.