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 a novel estimation method that effectively addresses complex models. Moreover, we develop a sensitivity analysis for possible violations of an identification assumption. 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.

在差异研究领域，人们越来越关注开发基于反事实的分解分析，以确定有助于减少人口差异的潜在中介机制。尽管该领域发展迅速，但大多数先前的研究仅限于基于回归的方法，从而破坏了解决具有多种中介和/或异质效应的复杂模型的可能性。我们提出了一种新的估计方法，可以有效地解决复杂模型。此外，我们对可能违反识别假设的情况进行了敏感性分析。