Three critical issues for causal inference that often occur in modern, complicated experiments are interference, treatment nonadherence, and missing outcomes. A great deal of research efforts has been dedicated to developing causal inferential methodologies that address these issues separately. However, methodologies that can address these issues simultaneously are lacking. We propose a Bayesian causal inference methodology to address this gap. Our methodology extends existing causal frameworks and methods, specifically, two-staged randomized experiments and the principal stratification framework. In contrast to existing methods that invoke strong structural assumptions to identify principal causal effects, our Bayesian approach uses flexible distributional models that can accommodate the complexities of interference and missing outcomes, and that ensure that principal causal effects are weakly identifiable. We illustrate our methodology via simulation studies and a re-analysis of real-life data from an evaluation of India's National Health Insurance Program. Our methodology enables us to identify new significant causal effects that were not identified in past analyses. Ultimately, our simulation studies and case study demonstrate how our methodology can yield more informative analyses in modern experiments with interference, treatment nonadherence, missing outcomes, and complicated outcome generation mechanisms.
翻译:现代、复杂实验中经常出现的因果关系推断的三个关键问题是干扰、不遵守治疗和缺失结果。许多研究工作都致力于制定分别解决这些问题的因果关系推断方法。然而,目前还缺乏同时解决这些问题的方法。我们建议采用巴伊西亚因果推断方法来弥补这一差距。我们的方法扩展了现有的因果框架和方法,具体地说,分为两个阶段的随机实验和主要分层框架。与援引强有力的结构假设来确定主要因果影响的现有方法不同,我们的巴伊西亚方法采用了灵活的分配模式,能够容纳干扰和缺失结果的复杂性,并确保主要因果影响可以薄弱地识别。我们通过模拟研究和从对印度国家健康保险方案的评价中重新分析真实生活数据的方法。我们的方法使我们能够查明过去分析中未查明的新的重大因果影响。最后,我们的模拟研究和案例研究表明,我们的方法如何在干扰、不合规、缺失结果和复杂结果产生机制的现代实验中产生更多的信息分析。