Interventional Approach for Path-Specific Effects

Journal of Causal Inference

article

Sheng-Hsuan Lin¹  Tyler VanderWeele² 

[1] Department of Epidemiology and Biostatistics, Harvard University, School of Public Health;Department of Epidemiology, Harvard School of Public Health

关键词: causal inference;    effect decomposition;    mediation analysis;    path-specific effect;    stochastic intervention;   

DOI  :  10.1515/jci-2015-0027

来源: De Gruyter

【 摘 要 】

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.

【 授权许可】

CC BY