Recent causal inference literature has introduced causal effect decompositions to quantify sources of observed inequalities or disparities in outcomes, but these approaches are typically limited to pairwise comparisons. In healthcare delivery settings, both the exposure of interest-hospital or healthcare unit-and sociodemographic group membership may be polytomous, making pairwise contrasts inadequate. We therefore take the observed variance in care delivery outcomes as the quantity of interest and develop a new causal variance decomposition framework for this setting. The proposed framework attributes the observed variation to eight components, including novel terms characterizing modification of hospital effects by sociodemographic group membership, hospital access or selection, and the correlation between these two sources of heterogeneity. We discuss the causal interpretation of these components, propose both parametric and nonparametric model-based estimators, and study their performance through simulation. Finally, we illustrate the method using data from the SEER program in an application to cervical cancer care delivery.

翻译：近期因果推断文献提出了因果效应分解方法，用于量化结果中观测到的不平等或差异的来源，但这些方法通常局限于两两比较。在医疗保健服务环境中，关注的暴露因素（医院或医疗单位）以及社会人口群体成员身份可能是多分类的，这使得两两对比不充分。因此，我们将医疗服务结果中的观测方差作为关注量，并针对该场景开发了一套新的因果方差分解框架。所提出的框架将观测变异分解为八个组成部分，包括描述医院效应因社会人口群体成员身份而改变的交互作用项、医院可及性或选择性差异项，以及这两个异质性来源之间的相关性项。我们讨论了这些组成部分的因果解释，提出了参数和非参数模型估计方法，并通过模拟研究评估其性能。最后，我们利用SEER项目数据，以宫颈癌医疗服务为例对该方法进行了说明。