  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.
