Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research

TL;DR

This paper introduces a new estimation method and sensitivity analysis for causal decomposition in disparities research, enabling analysis of complex models with multiple mediators and heterogeneity, demonstrated on cardiovascular health data.

Contribution

It presents a novel estimation approach and sensitivity analysis that address limitations of regression-based methods in complex, multi-mediator disparity models.

Findings

Quantifies the impact of education and discrimination on health disparities.

Demonstrates the method's effectiveness with US health data.

Provides insights into intersectional disparities in cardiovascular health.

Abstract

In the field of disparities research, there has been growing interest in developing a counterfactual-based decomposition analysis to identify underlying mediating mechanisms that help reduce disparities in populations. Despite rapid development in the area, most prior studies have been limited to regression-based methods, undermining the possibility of addressing complex models with multiple mediators and/or heterogeneous effects. We propose an estimation method that effectively addresses complex models. Moreover, we develop a novel sensitivity analysis for possible violations of identification assumptions. The proposed method and sensitivity analysis are demonstrated with data from the Midlife Development in the US study to investigate the degree to which disparities in cardiovascular health at the intersection of race and gender would be reduced if the distributions of education and perceived discrimination were the same across intersectional groups.