论文标题
荒地差异研究中因果分解的估计和灵敏度分析
Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research
论文作者
论文摘要
在差异研究领域，人们对开发基于反事实的分解分析的兴趣越来越多，以识别有助于减少人群差异的基本中介机制。尽管该地区的发展迅速，但大多数先前的研究都仅限于基于回归的方法，这破坏了解决具有多个介体和/或异质效应的复杂模型的可能性。我们提出了一种有效解决复杂模型的估计方法。此外，我们开发了一种新颖的灵敏度分析，以违反识别假设。通过美国研究的中年发展的数据证明了所提出的方法和敏感性分析，以研究如果教育和感知的歧视分布相同，则在种族和性别的交集中的心血管健康的差异将减少。
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.
