
It is often of interest in the health and social sciences to investigate the joint mediation effects of multiple post‐exposure mediating variables. Identification of such joint mediation effects generally require no unmeasured confounding of the outcome with respect to the whole set of mediators. As the number of mediators under consideration grows, this key assumption is likely to be violated as it is often infeasible to intervene on any of the mediators. In this article, we develop a simple two‐step method of moments estimation procedure to assess mediation with multiple mediators simultaneously in the presence of potential unmeasured mediator‐outcome confounding. Our identification result leverages heterogeneity of the population exposure effect on the mediators, which is plausible under a variety of empirical settings. The proposed estimators are illustrated through both simulations and an application to evaluate the mediating effects of post‐traumatic stress disorder symptoms in the association between self‐efficacy and fatigue among health care workers during the COVID‐19 outbreak.
FOS: Computer and information sciences, multiple mediators, Mediation Analysis, COVID-19, unmeasured confounding, Applications of statistics to biology and medical sciences; meta analysis, Disease Outbreaks, Methodology (stat.ME), causal mediation analysis, Humans, Statistics - Methodology, Fatigue
FOS: Computer and information sciences, multiple mediators, Mediation Analysis, COVID-19, unmeasured confounding, Applications of statistics to biology and medical sciences; meta analysis, Disease Outbreaks, Methodology (stat.ME), causal mediation analysis, Humans, Statistics - Methodology, Fatigue
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
