A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator-outcome confounders. In this paper, we propose a flexible causal decomposition analysis method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and studies of environmental pollutants. We extend a Monte Carlo-based causal decomposition analysis method to this setting by using a multivariate mediator model that can accommodate any combination of binary and continuous mediator variables. Furthermore, we state the causal assumptions needed to identify both joint and path-specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects under our proposed method, we perform a simulation study. We also apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black-White differences in incident diabetes using data from a national cohort study.

翻译：因果分解分析使研究者能够确定两组间健康结局的差异是否可归因于每组在一个或多个可修饰中介变量分布上的差异。借助这一分析结果，研究者和政策制定者可聚焦于针对这些中介变量设计干预措施。现有因果分解分析方法或仅关注单一中介变量，或假设各中介变量在给定组标签及中介-结局混杂因素条件下条件独立。本文提出一种灵活的因果分解分析方法，可适用于健康行为研究与环境污染研究中常见的多个相关且存在交互作用的中介变量情形。通过采用能够兼容二分类与连续型中介变量任意组合的多元中介模型，我们将基于蒙特卡洛的因果分解分析方法扩展至该场景。此外，我们阐明了通过每个中介变量识别联合效应及路径特异性分解效应所需的因果假设。为展示所提方法在减少分解效应偏倚和置信区间宽度方面的优势，我们进行了模拟研究。同时，基于一项全国性队列研究数据，我们应用该方法检验吸烟状况与膳食炎症评分的差异是否可解释黑人与白人群体在糖尿病发病率上的差异。