A review of causal estimation of effects in mediation analyses

Ten Have, Thomas R; Joffe, Marshall M

doi:10.1177/0962280210391076

Cited by 103Advanced Causal Inference TechniquesStatistical Methods in Clinical TrialsPhilosophy and History of Science

A review of causal estimation of effects in mediation analyses

Ten Have, Thomas R, Joffe, Marshall M·2012·Statistical Methods in Medical Research

DOI 10.1177/0962280210391076

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Abstract

We describe causal mediation methods for analysing the mechanistic factors through which interventions act on outcomes. A number of different mediation approaches have been presented in the biomedical, social science and statistical literature with an emphasis on different aspects of mediation. We review the different sets of assumptions that allow identification and estimation of effects in the simple case of a single intervention, a temporally subsequent mediator and outcome. These assumptions include various no confounding assumptions including sequential ignorability assumptions and also interaction assumptions involving the treatment and mediator. The understanding of such assumptions is crucial since some can be assessed under certain conditions (e.g. treatment–mediator interactions), whereas others cannot (sequential ignorability). These issues become more complex with multiple mediators and longitudinal outcomes. In addressing these assumptions, we review several causal approaches to mediation analyses.

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Mediation in the Causal Inference Framework
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Authors: Ten Have, T. R., & Joffe, M. M.
Affiliations: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
University of Pennsylvania (US)
Venue: Statistical Methods in Medical Research
Publisher: SAGE Publications
Published: 2010-12-16
Volume: 21
Issue: 1
Pages: 77-107
ISSN: 0962-2802 (ISSN-L)
1477-0334
DOI: 10.1177/0962280210391076
OpenAlex: W2013610310
Cited by: 103
References: 63
Citations by year: 2026: 3 · 2025: 4 · 2024: 11 · 2023: 4 · 2022: 3 · 2021: 9 · 2020: 2 · 2019: 6 · 2018: 9 · 2017: 6 · 2016: 5 · 2015: 11 · 2014: 12 · 2013: 6 · 2012: 11
FWCI: 4.87
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Statistical Methods in Medical Research (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Mental Health (R01MH078016, R01MH061892)
National Cancer Institute (R01CA095415)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords: causal mediation analysis, mediating effect, sequential ignorability, multiple mediators, longitudinal outcomes, intervention mechanisms, causal effect estimation
MeSH terms: Suicide Prevention, Data Interpretation, Statistical, Humans, Suicide, Models, Statistical, Bias, Causality, Confounding Factors, Epidemiologic, Biomedical Research

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A review of causal estimation of effects in mediation analyses

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