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Causal Mediation Analysis in Presence of Multiple Mediators Uncausally Related by Allan Jérolon; Laura Baglietto; Etienne Birmelé; Flora Alarcon; Vittorio Perduca is a Mathematics article available to read on EtoBox.

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## Abstract Mediation analysis aims at disentangling the effects of a treatment on an outcome through alternative causal mechanisms and has become a popular practice in biomedical and social science applications. The causal framework based on counterfactuals is currently the standard approach to mediation, with important methodological advances introduced in the literature in the last decade, especially for simple mediation, that is with one mediator at the time. Among a variety of alternative approaches, Imai et al. showed theoretical results and developed an R package to deal with simple mediation as well as with multiple mediation involving multiple mediators conditionally independent given the treatment and baseline covariates. This approach does not allow to consider the often encountered situation in which an unobserved common cause induces a spurious correlation between the mediators. In this context, which we refer to as mediation with uncausally related mediators, we show that, under appropriate hypothesis, the natural direct and joint indirect effects are non-parametrically identifiable. Moreover, we adopt the quasi-Bayesian algorithm developed by Imai et al. and propose

Who reads Causal Mediation Analysis in Presence of Multiple Mediators Uncausally Related?

Author

Allan Jérolon; Laura Baglietto; Etienne Birmelé; Flora Alarcon; Vittorio Perduca

Publisher

Walter de Gruyter GmbH

Published

2020

Language

EN

Field

Mathematics (Physical Sciences)