The 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. Analyses of principal causal effects from observed data in the literature mostly rely on ignorability of the treatment assignment, which requires practitioners to accurately measure as many as covariates so that all possible confounding sources are captured. However, collecting all potential confounders in observational studies is often difficult and costly, the ignorability assumption may thus be questionable. In this paper, by leveraging available negative controls that have been increasingly used to deal with uncontrolled confounding, we consider identification and estimation of causal effects when the treatment and principal strata are confounded by unobserved variables. Specifically, we show that the principal causal effects can be nonparametrically identified by invoking a pair of negative controls that are both required not to directly affect the outcome. We then relax this assumption and establish identification of principal causal effects under various semiparametric or parametric models. We also propose an estimation method of principal causal effects. Extensive simulation studies show good performance of the proposed approach and a real data application from the National Longitudinal Survey of Young Men is used for illustration.
翻译:对文献中观察到的数据的主要因果关系的分析大多依赖于无视治疗任务,这就要求从业人员精确地测量尽可能多的共变因素,以便捕捉所有可能的混淆来源。然而,在观察研究中收集所有潜在的混淆者往往困难和费用高昂,因此,忽略性假设可能令人怀疑。在本文件中,通过利用现有的消极控制手段,越来越多地用于处理失控的混结,我们考虑在治疗和主要层被未观察到的变量混为一团时确定和估计因果关系。具体地说,我们表明,主要因果影响可以通过采用不直接影响结果的对应消极控制来进行非对称性确定。我们随后放松这一假设,并在各种半偏差或准正反向性模型下确定主要因果影响。我们还提议了一种用于对正反向性模型进行真实性分析的模型。我们还提议了一种用于对正向性模型进行真实性分析的模型。