Choosing an Optimal Method for Causal Decomposition Analysis: A Better Practice for Identifying Contributing Factors to Health Disparities

TL;DR

This paper reviews existing methods for causal decomposition analysis of health disparities, introduces two new practical estimation techniques, and provides guidance for selecting optimal methods through simulations and real data application.

Contribution

It introduces two novel estimation methods for causal decomposition analysis, addressing limitations of existing approaches, especially for categorical mediators, and offers practical recommendations for method selection.

Findings

New imputation method handles categorical and continuous mediators.

A simple regression estimator performs well with bias and variance.

Application identifies mediators reducing health disparities.

Abstract

Causal decomposition analysis provides a way to identify mediators that contribute to health disparities between marginalized and non-marginalized groups. In particular, the degree to which a disparity would be reduced or remain after intervening on a mediator is of interest. Yet, estimating disparity reduction and remaining might be challenging for many researchers, possibly because there is a lack of understanding of how each estimation method differs from other methods. In addition, there is no appropriate estimation method available for a certain setting (i.e., a regression-based approach with a categorical mediator). Therefore, we review the merits and limitations of the existing three estimation methods (i.e., regression, weighting, and imputation) and provide two new extensions that are useful in practical settings. A flexible new method uses an extended imputation approach to address a categorical and continuous mediator or outcome while incorporating any nonlinear relationships. A new regression method provides a simple estimator that performs well in terms of bias and variance but at the cost of assuming linearity, except for exposure and mediator interactions. Recommendations are given for choosing methods based on a review of different methods and simulation studies. We demonstrate the practice of choosing an optimal method by identifying mediators that reduce race and gender disparity in cardiovascular health, using data from the Midlife Development in the US study.