Causal Mediation Analysis with Multiple Mediators.
mardi 26 juin 2018, 15h00 - 15h30
Causal mediation analysis is widely used in various domains such as biostatistics, epidemiology, psychology, legal and social sciences and public policy. The goal of such an analysis is to explain and quantify the effects of a variable on an outcome, directly and indirectly through other variables called mediators.
In 2010, Imaï and collaborators introduced a general framework to define, identify and estimate these effects and implemented their methods in the widely used R package « mediation ». When two or more mediators are considered, current approaches consists in repeating several simple mediator analysis in parallel. This could result in an estimation bias for quantities of interest effects.
In this work, contributions are threefold: First we show that conducting several simple mediator analysis in parallel, on data generating with multiple uncausally related mediators, result in a biased estimate. Then we propose a generalization of the approach by Imaï and collaborators in the case of multiple mediators uncausally related which lead to unbiased estimates. At last we implement our algorithm in R and apply it to simulate and real data.