School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, China.
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
Stat Med. 2024 Sep 30;43(22):4372-4387. doi: 10.1002/sim.10175. Epub 2024 Jul 29.
Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis of principal causal effects from observational studies mostly relies on the ignorability assumption of treatment assignment, which requires practitioners to accurately measure as many covariates as possible so that all potential sources of confounders are captured. However, in practice, collecting all potential confounding factors can be challenging and costly, rendering the ignorability assumption questionable. In this paper, we consider the identification and estimation of causal effects when treatment and principal stratification are confounded by unmeasured confounding. Specifically, we establish the nonparametric identification of principal causal effects using a pair of negative controls to mitigate unmeasured confounding, requiring they have no direct effect on the outcome variable. We also provide an estimation method for principal causal effects. Extensive simulations and a leukemia study are employed for illustration.
主分层已成为解决广泛类因果推理问题的一种流行工具，特别是在处理不依从和因死亡而截断问题时。在主层内的因果效应是由中间变量的联合潜在值决定的，也称为主要因果效应，这些效应在这些研究中通常是人们感兴趣的。来自观察性研究的主要因果效应分析主要依赖于治疗分配的可忽略性假设，这要求从业者尽可能准确地测量尽可能多的协变量，以便捕获所有潜在的混杂因素来源。然而，在实践中，收集所有潜在的混杂因素可能具有挑战性和成本高昂，使得可忽略性假设受到质疑。在本文中，我们考虑了当治疗和主分层受到未测量混杂因素的混杂时因果效应的识别和估计。具体来说，我们使用一对负对照来减轻未测量的混杂因素，建立了主要因果效应的非参数识别，要求它们对结果变量没有直接影响。我们还提供了一种主要因果效应的估计方法。通过广泛的模拟和白血病研究进行了说明。
