主分层已成为解决广泛的因果推理问题的流行工具，特别是在处理不合规和死亡截断问题时。主要层内的因果效应由中间变量的联合潜在值决定，也称为主因果效应，通常是这些研究中感兴趣的。对观察性研究的主要因果效应的分析主要依赖于治疗分配的可忽略性假设，这要求从业者准确测量尽可能多的协变量，以便捕获所有潜在的混杂因素。然而，在实践中，收集所有潜在的混杂因素可能具有挑战性且成本高昂，使得可忽略性假设值得怀疑。在本文中，我们考虑当治疗和主要分层被不可测量的混杂因素混淆时因果效应的识别和估计。具体来说，我们使用一对负控制来建立主要因果效应的非参数识别，以减轻不可测量的混杂因素，要求它们对结果变量没有直接影响。我们还提供了主要因果效应的估计方法。采用广泛的模拟和白血病研究进行说明。© 2024 John Wiley

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.© 2024 John Wiley & Sons Ltd.