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Bayesian Inference for the Causal Effect of Mediation
Author(s) -
Daniels Michael J.,
Roy Jason A.,
Kim Chanmin,
Hogan Joseph W.,
Perri Michael G.
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2012.01781.x
Subject(s) - causal inference , conditional independence , mediation , bayesian probability , sensitivity (control systems) , econometrics , bayesian inference , inference , independence (probability theory) , computer science , nonparametric statistics , statistics , artificial intelligence , mathematics , electronic engineering , political science , law , engineering
Summary We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess mediation in a recent weight management clinical trial.