Estimating the effects of randomized experiments and, by extension, their mediating mechanisms, is often complicated by treatment noncompliance. Two estimation methods for causal mediation in the presence of noncompliance have recently been proposed, the instrumental variable method (IV-mediate) and maximum likelihood method (ML-mediate). However, little research has examined their performance when certain assumptions are violated and under varying data conditions. This article addresses that gap in the research and compares the performance of the two methods. The results show that the distributional assumption of the compliance behavior plays an important role in estimation. That is, regardless of the estimation method or whether the other assumptions hold, results are biased if the distributional assumption is not met. We also found that the IV-mediate method is more sensitive to exclusion restriction violations, while the ML-mediate method is more sensitive to monotonicity violations. Moreover, estimates depend in part on compliance rate, sample size, and the availability and impact of control covariates. These findings are used to provide guidance on estimator selection.

中文翻译：

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假设对假设的因果平均调解效果估计方法的敏感性评估

估计随机实验的效果，以及扩展其介导机制的效果，通常会因治疗不合规而变得复杂。最近，提出了两种在不合规情况下进行因果调解的估计方法，即工具变量法（IV中介）和最大似然法（ML中介）。但是，当违反某些假设并在变化的数据条件下，很少有研究检查其性能。本文解决了研究中的空白，并比较了两种方法的性能。结果表明，合规行为的分布假设在估计中起着重要作用。也就是说，不管估计方法或其他假设是否成立，如果不满足分布假设，结果都会有偏差。我们还发现，IV中介方法对排除限制违反更加敏感，而ML中介方法对违反单调性更加敏感。此外，估计值部分取决于合规率，样本量以及控制变量的可用性和影响。这些发现可用来为估算器选择提供指导。