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In the field of disparities research, there has been growing interest indeveloping a counterfactual-based decomposition analysis to identify underlyingmediating mechanisms that help reduce disparities in populations. Despite rapiddevelopment in the area, most prior studies have been limited toregression-based methods, undermining the possibility of addressing complexmodels with multiple mediators and/or heterogeneous effects. We propose anestimation method that effectively addresses complex models. Moreover, wedevelop a novel sensitivity analysis for possible violations of identificationassumptions. The proposed method and sensitivity analysis are demonstrated withdata from the Midlife Development in the US study to investigate the degree towhich disparities in cardiovascular health at the intersection of race andgender would be reduced if the distributions of education and perceiveddiscrimination were the same across intersectional groups.

Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research