Examining the Efficacy of Coarsened Exact Matching as an Alternative to Propensity Score Matching

TL;DR

This paper critically compares coarsened exact matching (CEM) and propensity score matching (PSM), showing that PSM generally outperforms CEM in reducing imbalance, bias, and model dependence, especially with many covariates.

Contribution

The study provides a comprehensive analysis demonstrating that CEM is less effective than PSM in various scenarios and highlights limitations of CEM in high-dimensional settings.

Findings

PSM reduces imbalance more effectively than CEM.

CEM exhibits greater bias and residual confounding.

PSM is more robust to model misspecification.

Abstract

Coarsened exact matching (CEM) is often promoted as a superior alternative to propensity score matching (PSM) for addressing imbalance, model dependence, bias, and efficiency. However, this recommendation remains uncertain. First, CEM is commonly mischaracterized as exact matching, despite relying on coarsened rather than original variables. This inexactness in matching introduces residual confounding, which necessitates accurate modeling of the outcome-confounder relationship post-matching to mitigate bias, thereby increasing vulnerability to model misspecification. Second, prior studies overlook that any imbalance between treated and untreated subjects matched on the same propensity score is attributable to random variation. Thus, claims that CEM outperforms PSM in reducing imbalance are unfounded, particularly when using metrics like Mahalanobis distance, which do not account for chance imbalance in PSM. Our simulations show that PSM reduces imbalance more effectively than CEM when evaluated with multivariate standardized mean differences (SMD), and unadjusted analyses indicate greater bias with CEM. While adjusted analyses in both CEM with autocoarsening and PSM may perform similarly when matching on few variables, CEM suffers from the curse of dimensionality as the number of factors increases, resulting in substantial data loss and unstable estimates. Increasing the level of coarsening may mitigate data loss but exacerbates residual confounding and model dependence. In contrast, both analytical results and simulations demonstrate that PSM is more robust to model misspecification and thus less model-dependent. Therefore, CEM is not a viable alternative to PSM when matching on a large number of covariates.