Mendelian randomization as an instrumental variable approach to causal inference
Open accessAbstract
In epidemiological research, the causal effect of a modifiable phenotype or exposure on a disease is often of public health interest. Randomized controlled trials to investigate this effect are not always possible and inferences based on observational data can be confounded. However, if we know of a gene closely linked to the phenotype without direct effect on the disease, it can often be reasonably assumed that the gene is not itself associated with any confounding factors — a phenomenon called Mendelian randomization. These properties define an instrumental variable and allow estimation of the causal effect, despite the confounding, under certain model restrictions. In this paper, we present a formal framework for causal inference based on Mendelian randomization and suggest using directed acyclic graphs to check model assumptions by visual inspection. This framework allows us to address limitations of the Mendelian randomization technique that have often been overlooked in the medical literature.
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The instrumental variable assumptions are: association with the exposure of interest; lack of association with any confounder of the exposure-outcome relationship, including unmeasured confounders; and lack of conditional association with the outcome given the exposure and all confounders.
“These variants must satisfy the assumptions of an instrumental variable (IV): association with the exposure of interest; lack of association with any confounder of the exposure–outcome relationship (including those that are unmeasured); and lack of conditional association with the outcome given the exposure and all the confounders.2”
From Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways. (2015) · cites this paper as [2]
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- Authors
- Didelez, V., & Sheehan, N.
- Affiliations
- University College London (GB)
- University of Leicester (GB)
- Corresponding author
- Sheehan, Nuala
- Venue
- Statistical Methods in Medical Research
- Publisher
- SAGE Publications
- Published
- 2007-08
- Volume
- 16
- Issue
- 4
- Pages
- 309-330
- ISSN
- 0962-2802 (ISSN-L)
- 1477-0334
- OpenAlex
- W2129465958
- Cited by
- 923
- References
- 59
- Citations by year
- 2026: 35 · 2025: 70 · 2024: 149 · 2023: 97 · 2022: 74 · 2021: 54 · 2020: 85 · 2019: 38 · 2018: 48 · 2017: 39 · 2016: 33 · 2015: 40 · 2014: 34 · 2013: 24 · 2012: 30
- FWCI
- 17.29
- Type
- journal article
- Language
- en
- Open access
- green
- smm.sagepub.com
- Locations
- Statistical Methods in Medical Research (publishedVersion)
- PubMed (publishedVersion)
- Leicester Research Archive (University of Leicester) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion)
- Funders
- Leverhulme Trust
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, instrumental variables, causal inference, confounding, directed acyclic graphs, model assumptions, genetic variants, observational data, causal effect estimation
- MeSH terms
- Animals, Epidemiologic Methods, Epidemiology, Humans, Random Allocation, Causality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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