Estimation of Direct Causal Effects
Abstract
Many common problems in epidemiologic and clinical research involve estimating the effect of an exposure on an outcome while blocking the exposure's effect on an intermediate variable. Effects of this kind are termed direct effects. Estimation of direct effects is typically the goal of research aimed at understanding mechanistic pathways by which an exposure acts to cause or prevent disease, as well as in many other settings. Although multivariable regression is commonly used to estimate direct effects, this approach requires assumptions beyond those required for the estimation of total causal effects. In addition, when the exposure and intermediate variables interact to cause disease, multivariable regression estimates a particular type of direct effect-the effect of an exposure on an outcome when the intermediate is fixed at a specified level. Using the counterfactual framework, we distinguish this definition of a direct effect (controlled direct effect) from an alternative definition, in which the effect of the exposure on the intermediate is blocked, but the intermediate is otherwise allowed to vary as it would in the absence of exposure (natural direct effect). We illustrate the difference between controlled and natural direct effects using several examples. We present an estimation approach for natural direct effects that can be implemented using standard statistical software, and we review the assumptions underlying our approach (which are less restrictive than those proposed by previous authors).
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The total effect of an exposure on an outcome can be decomposed into a direct and an indirect effect when the effect is partially mediated by another risk factor.
“The total effect of the exposure on the outcome can therefore be decomposed into a direct and an indirect effect.11,12”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [12]
The total effect of an exposure on an outcome can be decomposed into a direct and an indirect effect when the effect is partially mediated by another risk factor.
“The total effect of the exposure on the outcome can therefore be decomposed into a direct and an indirect effect.11,12”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [11]
The total effect of the exposure on the outcome is equal to the sum of the natural direct and indirect effects.
“The total effect of the exposure on the outcome is equal to the sum of the natural direct and indirect effects.11,13”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [11]
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- Authors
- Petersen, M. L., Sinisi, S. E., & van der Laan, M. J.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Petersen, Maya L.
- ORCID
- Petersen, Maya L.
- Venue
- Epidemiology
- Published
- 2006-05
- Volume
- 17
- Issue
- 3
- Pages
- 276-284
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2160610058
- Cited by
- 362
- References
- 17
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 6 · 2023: 9 · 2022: 16 · 2021: 14 · 2020: 18 · 2019: 17 · 2018: 19 · 2017: 24 · 2016: 11 · 2015: 21 · 2014: 27 · 2013: 21 · 2012: 32
- FWCI
- 15.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM071397)
- 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
- natural direct effect, controlled direct effect, intermediate variables, counterfactual framework, causal inference, mechanistic pathways
- MeSH terms
- Clinical Trials as Topic, Humans, Research Design, United States, Models, Statistical, Effect Modifier, Epidemiologic, Epidemiologic Studies
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