Identification of Natural Direct Effects When a Confounder of the Mediator Is Directly Affected by Exposure
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Natural direct and indirect effects formalize traditional notions of mediation analysis into a rigorous causal framework and have recently received considerable attention in epidemiology and in social sciences. Sufficient conditions for the identification of natural direct effects were formulated by Judea Pearl under a nonparametric structural equations model, which assumes certain independencies between potential outcomes. A common situation in epidemiology is that a confounder of the mediator-outcome relationship is itself affected by the exposure, in which case natural direct effects fail to be nonparametrically identified without additional assumptions, even under Pearl's nonparametric structural equations model. In this article, we show that when a single binary confounder of the mediator is affected by the exposure, the natural direct effect is nonparametrically identified under the model, assuming monotonicity about the effect of the exposure on the confounder. A similar result is shown to hold for a vector of binary confounders of the mediator under a certain independence assumption about the confounders. Finally, we show that natural direct effects are more generally identified if there is no additive mean interaction between the mediator and the confounders of the mediator affected by exposure. When correct, this latter assumption is particularly appealing because it does not require monotonicity of effects of the exposure. In addition, it places no restriction on the nature of the confounders of the mediator, which can be continuous or polytomous.
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If there are post-treatment confounders, the natural direct effect cannot in general be identified without further assumptions, even if these variables are measured.
“If there are post-treatment confounders U* (Figure 3), then the natural direct effect cannot in general be identified (even if these variables are measured20) without further assumptions.21”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [21]
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- Authors
- Tchetgen Tchetgen, E. J., & VanderWeele, T. J.
- Affiliations
- Harvard University (US)
- Venue
- Epidemiology
- Published
- 2014-03
- Volume
- 25
- Issue
- 2
- Pages
- 282-291
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC4230499
- OpenAlex
- W2061754265
- Cited by
- 93
- References
- 27
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 7 · 2023: 4 · 2022: 8 · 2021: 4 · 2020: 6 · 2019: 6 · 2018: 4 · 2017: 9 · 2016: 6 · 2015: 8 · 2014: 9 · 2012: 3
- FWCI
- 11.28
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (P01CA134294, R35CA197449)
- National Institute of Allergy and Infectious Diseases (R01AI104459)
- National Institute of Environmental Health Sciences (R01ES020337)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Keywords
- natural direct effect, mediation analysis, nonparametric identification, monotonicity assumption
- MeSH terms
- Data Interpretation, Statistical, Models, Statistical, Epidemiologic Research Design, Confounding Factors, Epidemiologic, Effect Modifier, Epidemiologic
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