Mendelian randomization as an instrumental variable approach to causal inference

2007/08/01 by Vanessa Didelez, Nuala A. Sheehan, Nuala Sheehan · 8 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Advanced Causal Inference Techniques #Genetic Associations and Epidemiology #Statistical Methods in Clinical Trials

paper · doi:10.1177/0962280206077743

openalex publication_date 2007/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

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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