孟德尔随机化
因果推理
混淆
工具变量
观察研究
推论
计量经济学
计算机科学
因果关系(物理学)
统计
人工智能
数学
生物
遗传学
基因
遗传变异
基因型
物理
量子力学
作者
Vanessa Didelez,Nuala A. Sheehan
标识
DOI:10.1177/0962280206077743
摘要
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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