The incorporation of causal inference in mediation analysis has led to theoretical and methodological advancements-effect definitions with causal interpretation, clarification of assumptions required for effect identification, and an expanding array of options for effect estimation. However, the literature on these results is fast-growing and complex, which may be confusing to researchers unfamiliar with causal inference or unfamiliar with mediation. The goal of this article is to help ease the understanding and adoption of causal mediation analysis. It starts by highlighting a key difference between the causal inference and traditional approaches to mediation analysis and making a case for the need for explicit causal thinking and the causal inference approach in mediation analysis. It then explains in as-plain-as-possible language existing effect types, paying special attention to motivating these effects with different types of research questions, and using concrete examples for illustration. This presentation differentiates 2 perspectives (or purposes of analysis): the explanatory perspective (aiming to explain the total effect) and the interventional perspective (asking questions about hypothetical interventions on the exposure and mediator, or hypothetically modified exposures). For the latter perspective, the article proposes tapping into a general class of interventional effects that contains as special cases most of the usual effect types-interventional direct and indirect effects, controlled direct effects and also a generalized interventional direct effect type, as well as the total effect and overall effect. This general class allows flexible effect definitions which better match many research questions than the standard interventional direct and indirect effects. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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为应用研究人员澄清因果中介分析：根据我们想要学习的内容定义效果。

中介分析中因果推理的结合带来了理论和方法上的进步——具有因果解释的效应定义、效应识别所需假设的澄清以及效应估计选项的扩展。然而，关于这些结果的文献增长迅速且复杂，这可能会让不熟悉因果推理或不熟悉中介的研究人员感到困惑。本文的目标是帮助简化因果中介分析的理解和采用。首先强调因果推理与传统中介分析方法之间的关键区别，并论证中介分析中明确因果思维和因果推理方法的必要性。然后用尽可能通俗易懂的语言解释现有的效果类型，特别注意用不同类型的研究问题激发这些效果，并用具体的例子进行说明。本演示区分了两种观点（或分析目的）：解释性观点（旨在解释总体效果）和干预性观点（询问有关暴露和中介的假设干预措施，或假设修改的暴露）。对于后一种观点，本文建议利用一类干预效应，其中包含大多数常见效应类型的特殊情况——干预直接效应和间接效应、受控直接效应以及广义干预直接效应类型，以及总效应和总效应。这个通用类别允许灵活的效果定义，比标准的干预直接和间接效果更好地匹配许多研究问题。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。