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 active 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推断的三个关键问题是干扰、治疗不依从和结果缺失。大量研究工作致力于开发分别解决这些问题的因果推断方法。然而，能够同时处理这些问题的研究方法仍然缺乏。我们提出了一种贝叶斯因果推断方法来填补这一空白。我们的方法扩展了现有的因果框架和方法，特别是两阶段随机实验和主分层框架。与现有方法通过强结构假设来识别主因果效应不同，我们的贝叶斯方法采用灵活的分布模型，既能适应干扰和结果缺失的复杂性，又能确保主因果效应具备弱可识别性。我们通过模拟研究和对印度国家健康保险计划评估的真实数据再分析来展示该方法。该方法使我们能够识别出以往分析中未被发现的新活性因果效应。最终，我们的模拟研究和案例研究表明，在存在干扰、治疗不依从、结果缺失和复杂结果生成机制的现代实验中，该方法能产生更具信息量的分析结果。