健康差异研究中因果分解的估计与敏感性分析
Estimation and Sensitivity Analysis for Causal Decomposition in Heath Disparity Research
摘要：
在差异研究领域，人们越来越有兴趣开发一种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。尽管在该领域的快速发展，大多数先前的研究已被限制为基于回归的方法，破坏了解决复杂的模型与多个介质和/或异质性的影响的可能性。我们提出了一种有效地解决复杂模型的估计方法。此外，我们开发了一种新的敏感性分析可能违反识别假设。所提出的方法和敏感性分析证明与数据的中年发展在美国的研究，以调查在何种程度上的差距，心血管健康的种族和性别的交叉将减少，如果分布的教育和感知的歧视是相同的交叉组。
英文摘要：
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.
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