hen multiple mediators exist on the causal pathway from treatment to outcome, path analysis prevails for disentangling indirect effects along paths linking possibly several mediators. However, separately evaluating each indirect effect along different posited paths demands stringent assumptions, such as correctly specifying the mediators’ causal structure, and no unobserved confounding among the mediators. These assumptions may be unfalsifiable in practice and, when they fail to hold, can result in misleading conclusions about the mediators. Nevertheless, these assumptions are avoidable when substantive interest is in inference about the indirect effects specific to each distinct mediator. In this article, we introduce a new definition of indirect effects called interventional indirect effects from the causal inference and epidemiology literature. Interventional indirect effects can be unbiasedly estimated without the assumptions above while retaining scientifically meaningful interpretations. We show that under a typical class of linear and additive mean models, estimators of interventional indirect effects adopt the same analytical form as prevalent product-of-coefficient estimators assuming a parallel mediator model. Prevalent estimators are therefore unbiased when estimating interventional indirect effects—even when there are unknown causal effects among the mediators—but require a different causal interpretation. When other mediators moderate the effect of each mediator on the outcome, and the mediators’ covariance is affected by treatment, such an indirect effect due to the mediators’ mutual dependence (on one another) cannot be attributed to any mediator alone. We exploit the proposed definitions of interventional indirect effects to develop novel estimators under such settings. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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在不假设中介之间存在任何因果结构的情况下，通过多个中介消除间接影响。

当从治疗到结果的因果路径上存在多种中介时，路径分析占主导地位，用于沿着可能连接多个中介的路径解开间接影响。然而，沿着不同的假定路径分别评估每个间接影响需要严格的假设，例如正确指定中介的因果结构，以及中介之间没有未观察到的混淆。这些假设在实践中可能是不可证伪的，并且当它们不成立时，可能会导致对调解人的误导性结论。然而，当实质性兴趣在于推断每个不同调解者特有的间接影响时，这些假设是可以避免的。在这篇文章中，我们从因果推理和流行病学文献中引入了一种新的间接影响定义，称为干预性间接影响。在保留具有科学意义的解释的情况下，可以在没有上述假设的情况下无偏见地估计干预的间接影响。我们表明，在一类典型的线性和加法均值模型下，干预间接效应的估计采用与假设平行中介模型的普遍系数乘积估计相同的分析形式。因此，在估计干预的间接影响时，普遍的估计量是无偏的——即使在调解人之间存在未知的因果关系时——但需要不同的因果解释。当其他中介调节每个中介对结果的影响，并且中介的协方差受治疗影响时，由于中介相互依赖（彼此）而产生的这种间接影响不能单独归因于任何中介。我们利用建议的干预间接效应定义来开发此类环境下的新型估计量。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）