Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (PM2.5) increases mortality risk. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial role in understanding vulnerability to air pollution. To inform policy, it is necessary to identify mutually exclusive groups of the population that are more or less vulnerable to air pollution. In the causal inference literature, the Conditional Average Treatment Effect (CATE) is a commonly used metric designed to characterize the heterogeneity of a treatment effect based on some population characteristics. In this work, we introduce a novel modeling approach, called Confounder-Dependent Bayesian Mixture Model (CDBMM), to characterize causal effect heterogeneity. More specifically, our method leverages the flexibility of the Dependent Dirichlet Process to model the distribution of the potential outcomes conditionally to the covariates, thus enabling us to: (i) estimate individual treatment effects, (ii) identify heterogeneous and mutually exclusive population groups defined by similar CATEs, and (iii) estimate causal effects within each of the identified groups. Through simulations, we demonstrate the effectiveness of our method in uncovering key insights about treatment effects heterogeneity. We apply our method to claims data from Medicare enrollees in Texas. We found seven mutually exclusive groups where the causal effects of PM2.5 on mortality are heterogeneous.
翻译:一些流行病学研究证明,长期接触微粒物质(PM2.5)会增加死亡率风险,此外,一些人口特征(如年龄、种族和社会经济地位)在了解空气污染脆弱性方面可能发挥至关重要的作用,为政策提供信息,有必要确定多少容易受空气污染的相互排斥的人口群体。在因果推断文献中,条件平均治疗效应(CATE)是一种常用的衡量标准,旨在根据某些人口特征确定治疗效应的异质性。此外,在这项工作中,我们采用了一种新型的模型化方法,称为Confounder-Dependent Bayesian Mixture模型(CDBMMM),以说明因果关系差异性。更具体地说,我们的方法是利用Deptent Diripllet进程的灵活性,以有条件地模拟潜在结果的分配,从而使我们能够:(一) 估计个人治疗效应,(二) 确定由类似Cates界定的异性和相互排斥性人口群体,(三) 估计每个被识别的Bayesturemultural 模型(Centrialality Socality)的因果关系影响,我们通过模拟方法,从Creal-deal-dealdealalalalalitystalmental customisquestations),我们发现了我们发现了Crequestal 。