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Interventional Approach for Path-Specific Effects
Journal of Causal Inference ( IF 1.4 ) Pub Date : 2017-01-10 , DOI: 10.1515/jci-2015-0027
Sheng-Hsuan Lin 1 , Tyler VanderWeele 2
Affiliation  

Standard causal mediation analysis decomposes the total effect into a direct effect and an indirect effect in settings with only one single mediator. Under the settings with multiple mediators, all mediators are often treated as one single block of mediators. The effect mediated by a certain combination of mediators, i. e. path-specific effect (PSE), is not always identifiable without making strong assumptions. In this paper, the authors propose a method, defining a randomly interventional analogue of PSE (rPSE), as an alternative approach for mechanism investigation. This method is valid under assumptions of no unmeasured confounding and allows settings with mediators dependent on each other, interaction, and mediator-outcome confounders which are affected by exposure. In addition, under linearity and no-interaction, our method has the same form of traditional path analysis for PSE. Furthermore, under single mediator without a mediator-outcome confounder affected by exposure, it also has the same form of the results of causal mediation analysis. We also provide SAS code for settings of linear regression with exposure-mediator interaction and perform analysis in the Framingham Heart Study dataset, investigating the mechanism of smoking on systolic blood pressure as mediated by both cholesterol and body weight. Allowing decomposition of total effect into several rPSEs, our method contributes to investigation of complicated causal mechanisms in settings with multiple mediators.

中文翻译:

路径特异性影响的干预方法

标准因果中介分析将总效应分解为在只有一个中介者的情况下的直接效应和间接效应。在具有多个中介者的设置下,所有中介者通常被视为一个单一的中介者块。由某种中介因素组合介导的效应,即路径特异性效应 (PSE),如果不做出强有力的假设,并不总是可以识别的。在本文中,作者提出了一种方法,定义 PSE 的随机介入类似物 (rPSE),作为机制研究的替代方法。这种方法在没有不可测量的混杂因素的假设下是有效的,并且允许设置具有相互依赖的中介、相互作用和受暴露影响的中介结果混杂因素。此外,在线性和无交互作用下,我们的方法与 PSE 的传统路径分析形式相同。此外,在没有受暴露影响的中介-结果混杂因素的单一中介下,也具有相同形式的因果中介分析结果。我们还提供了 SAS 代码,用于设置线性回归与暴露介质相互作用,并在弗雷明汉心脏研究数据集中进行分析,研究吸烟对由胆固醇和体重介导的收缩压的机制。允许将总效应分解为几个 rPSE,我们的方法有助于在具有多个介体的环境中研究复杂的因果机制。它也具有相同形式的因果中介分析结果。我们还提供了 SAS 代码,用于设置线性回归与暴露介体相互作用,并在弗雷明汉心脏研究数据集中进行分析,研究吸烟对由胆固醇和体重介导的收缩压的机制。允许将总效应分解为几个 rPSE,我们的方法有助于在具有多个介体的环境中研究复杂的因果机制。它也具有相同形式的因果中介分析结果。我们还提供了 SAS 代码,用于设置线性回归与暴露介体相互作用,并在弗雷明汉心脏研究数据集中进行分析,研究吸烟对由胆固醇和体重介导的收缩压的机制。允许将总效应分解为几个 rPSE,我们的方法有助于在具有多个介体的环境中研究复杂的因果机制。
更新日期:2017-01-10
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