Using propensity scores for racial disparities analysis

TL;DR

This paper advocates for using propensity scores as a covariate balancing tool to analyze racial disparities in health care, clarifying misconceptions and demonstrating methods with real data re-analysis.

Contribution

It clarifies the use of propensity scores for racial disparities analysis, detailing estimands, assumptions, and combining weighting with adjustment methods.

Findings

Propensity scores can effectively analyze racial disparities with weaker assumptions.

Three weighting strategies are described: overlap, inverse probability, and ATE for treated.

Re-analysis of survey data demonstrates the method's practical application.

Abstract

Propensity score plays a central role in causal inference, but its use is not limited to causal comparisons. As a covariate balancing tool, propensity score can be used for controlled descriptive comparisons between groups whose memberships are not manipulable. A prominent example is racial disparities in health care. However, conceptual confusion and hesitation persists for using propensity score in racial disparities studies. In this commentary, we argue that propensity score, possibly combined with other methods, is an effective tool for racial disparities analysis. We describe relevant estimands, target population, and assumptions. In particular, we clarify that a controlled descriptive comparisons require weaker assumptions than a causal comparison. We discuss three common propensity score weighting strategies: overlap weighting, inverse probability weighting and average treatment effect for treated weighting. We further describe how to combine weighting with the rank-and-replace adjustment method to produce racial disparity estimates concordant to the Institute of Medicine's definition. The method is illustrated by a re-analysis of the Medical Expenditure Panel Survey data.