Abstract Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of treatment noncompliance. Existing studies based on the IV-based method focus on identifying the mediated portion of the intention-to-treat effect, which relies on several identification assumptions. However, little attention has been given to assessing the sensitivity of the identification assumptions or mitigating the impact of violating these assumptions. This study proposes a two-stage joint modeling method for conducting causal mediation analysis in the presence of treatment noncompliance, in which modeling assumptions can be employed to decrease the sensitivity to violation of some identification assumptions. The use of a joint modeling method is also conducive to conducting sensitivity analyses to the violation of identification assumptions. We demonstrate our approach using the Jobs II data, in which the effect of job training on job seekers’ mental health is examined.

中文翻译：

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存在治疗不依从的因果调解分析的两阶段联合建模方法

摘要 估计随机治疗的效果和通过中介传递的效果通常会因治疗不依从而变得复杂。在文献中，已经开发了一种基于工具变量 (IV) 的方法来研究存在治疗不依从性时的因果中介效应。基于 IV 方法的现有研究侧重于确定意向治疗效应的中介部分，这依赖于几个识别假设。然而，很少有人关注评估识别假设的敏感性或减轻违反这些假设的影响。本研究提出了一种两阶段联合建模方法，用于在存在治疗不依从的情况下进行因果中介分析，其中可以使用建模假设来降低对违反某些识别假设的敏感性。联合建模方法的使用也有利于对违反识别假设的情况进行敏感性分析。我们使用 Jobs II 数据展示了我们的方法，其中检查了工作培训对求职者心理健康的影响。