Interpretation and Identification of Causal Mediation
Interpretation and Identification of Causal Mediation
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DOI：
10.1037/a0036434
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发表时间：
2014-12-01
影响因子：
7
通讯作者：
Pearl, Judea
中科院分区：
文献类型：
--
作者：
Pearl, Judea
本文回顾了因果调解分析的基础，并对确定自然的直接和间接影响所需的条件进行了全面和透明的说明，从而有助于在具体应用中更明智地判断这些条件的合理性。我指出，文献中通常引用的条件过于严格，可以在不损害身份识别的情况下大幅放宽。特别是，我指出，自然影响可以通过超出混杂因素标准调整的方法来识别，适用于治疗分配与介体或结果仍有混淆的观察性研究。这些识别条件可以从模型的图解描述中通过算法进行验证，并保证只要描述正确，就会产生无偏见的结果。在参数模型中，识别条件可以进一步放宽，可能包括相互作用，并允许比较由相互依赖变量调节的几条路径的相对重要性。
This article reviews the foundations of causal mediation analysis and offers a general and transparent account of the conditions necessary for the identification of natural direct and indirect effects, thus facilitating a more informed judgment of the plausibility of these conditions in specific applications. I show that the conditions usually cited in the literature are overly restrictive and can be relaxed substantially without compromising identification. In particular, I show that natural effects can be identified by methods that go beyond standard adjustment for confounders, applicable to observational studies in which treatment assignment remains confounded with the mediator or with the outcome. These identification conditions can be validated algorithmically from the diagrammatic description of one's model and are guaranteed to produce unbiased results whenever the description is correct. The identification conditions can be further relaxed in parametric models, possibly including interactions, and permit one to compare the relative importance of several pathways, mediated by interdependent variables.