因果推理
观察研究
因果关系(物理学)
推论
统计推断
数据科学
工具箱
统计模型
因果模型
统计遗传学
生物
计算机科学
基因组学
人工智能
计量经济学
遗传学
医学
病理
基因组
物理
经济
统计
程序设计语言
基因
数学
量子力学
作者
Jean‐Baptiste Pingault,Paul O’Reilly,Tabea Schoeler,George B. Ploubidis,Frühling Rijsdijk,Frank Dudbridge
标识
DOI:10.1038/s41576-018-0020-3
摘要
Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology — including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining — has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox. Various types of observational studies can provide statistical associations between factors, such as between an environmental exposure and a disease state. This Review discusses the various genetics-focused statistical methodologies that can move beyond mere associations to identify (or refute) various mechanisms of causality, with implications for responsibly managing risk factors in health care and the behavioural and social sciences.
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