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Causal mediation analysis with sure outcomes of random events model
Author(s) -
Li Wei,
Geng Zhi,
Zhou XiaoHua
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9009
Subject(s) - identifiability , outcome (game theory) , mediation , econometrics , estimator , causal inference , confounding , contrast (vision) , computer science , statistics , mathematics , artificial intelligence , mathematical economics , political science , law
Abstract Mediation analysis is a useful tool in randomized trials for understanding how a treatment works, in particular how much of the treatment's effect on an outcome is explained by a mediator variable. The traditional approach to mediation analysis makes sequential ignorability assumption which precludes the existence of unobserved confounders between the mediator and outcome variables. Since the randomized experiment does not randomize the mediator, sequential ignorability may not be plausible. In this article, based on a statistical model termed sure outcomes of random events model, we propose an alternative approach to causal mediation analysis without relying on the sequential ignorability assumption for the case of binary treatment and mediator variables. When the outcome is also binary, we establish the identifiability of the average natural direct and indirect effects in the presence of an unobserved confounder between mediator and outcome variables. More importantly, if the identifiability conditions are violated, we provide new bounds that are narrower than those in the previous studies, and these bound results are extended to the case of an arbitrary bounded outcome. Simulation studies show good performance for the proposed estimators in finite samples. Finally, we use a job training intervention on the mental health study to illustrate our approach.