Cited by 858Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyBayesian Modeling and Causal Inference

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Mediation Analysis with Multiple Mediators

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VanderWeele, Tyler, Vansteelandt, Stijn·2014·Epidemiologic Methods

DOI 10.1515/em-2012-0010PMC PMC4287269

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Abstract

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.

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Additional file 5 of An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs
Caubet, M., L’Espérance, K., Koushik, A., & Lefebvre, G. (2024) · Figshare · doi:10.6084/m9.figshare.26695779
Additional file 1 of History of childhood maltreatment associated with hospitalization or death due to COVID-19: a cohort study
Wang, Y., Ge, F., Aspelund, T., Ask, H., Hauksdóttir, A., Hu, K., Jakobsdóttir, J., Zoega, H., Shen, Q., Whalley, H. C., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26758523
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Additional file 4 of An empirical evaluation of approximate and exact regression-based causal mediation approaches for a binary outcome and a continuous or a binary mediator for case-control study designs
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Authors: VanderWeele, T., & Vansteelandt, S.
Affiliations: Harvard University (US)
Ghent University (BE)
Corresponding author: VanderWeele, Tyler
ORCID: VanderWeele, Tyler
Vansteelandt, Stijn
Venue: Epidemiologic Methods
Publisher: Walter de Gruyter GmbH
Published: 2014-01-03
Volume: 2
Issue: 1
Pages: 95-115
ISSN: 2161-962X (ISSN-L)
2194-9263
DOI: 10.1515/em-2012-0010
PMC: PMC4287269
OpenAlex: W2009754435
Cited by: 858
References: 23
Citations by year: 2026: 62 · 2025: 96 · 2024: 140 · 2023: 97 · 2022: 106 · 2021: 95 · 2020: 85 · 2019: 64 · 2018: 40 · 2017: 39 · 2016: 16 · 2015: 13 · 2014: 4
FWCI: 25.37
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Epidemiologic Methods (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Ghent University Academic Bibliography (Ghent University) (submittedVersion)
Funders: National Institute of Environmental Health Sciences (R01ES017876)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: mediation analysis, multiple mediators, direct and indirect effects, count outcomes, causal inference, regression estimation

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