论文标题
朝着健康公平的新因果分解范式
A New Causal Decomposition Paradigm towards Health Equity
论文作者
论文摘要
因果分解通过评估每个调解人引起的差异的比例，为分析健康差异问题提供了强大的工具。但是，这些方法中的大多数都缺乏\ emph {策略含义}，因为它们无法解释由调解员造成的所有差异来源。此外，他们的估计值\ emph {预先指定}某些协变量集（\ emph {a.k.a}，可允许的集合）对于强可忽略性条件），这可能会出现问题，因为该集合中的某些变量可能会诱发新的假性特征。为了解决这些问题，在结构性因果模型的框架下，我们建议将总效应分解为调整后和未调节的效果，而前者能够通过调整每个调解人从弱势群体到优势的群体的分布来包括所有类型的差异。此外，配备了最大祖传图和上下文变量，我们可以自动识别可允许的集合，然后使用有效的算法进行估计。理论上的正确性和我们方法的功效在合成数据集和脊柱疾病数据集上得到了证明。
Causal decomposition has provided a powerful tool to analyze health disparity problems, by assessing the proportion of disparity caused by each mediator. However, most of these methods lack \emph{policy implications}, as they fail to account for all sources of disparities caused by the mediator. Besides, their estimations \emph{pre-specified} some covariates set (\emph{a.k.a}, admissible set) for the strong ignorability condition to hold, which can be problematic as some variables in this set may induce new spurious features. To resolve these issues, under the framework of the structural causal model, we propose to decompose the total effect into adjusted and unadjusted effects, with the former being able to include all types of disparity by adjusting each mediator's distribution from the disadvantaged group to the advantaged ones. Besides, equipped with maximal ancestral graph and context variables, we can automatically identify the admissible set, followed by an efficient algorithm for estimation. Theoretical correctness and the efficacy of our method are demonstrated on a synthetic dataset and a spine disease dataset.
