A review of causal inference for biomedical informatics

被引:108
作者
Kleinberg, Samantha [1 ]
Hripcsak, George [1 ]
机构
[1] Columbia Univ, Dept Biomed Informat, New York, NY 10032 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
Causal inference; Causal explanation; Electronic health records; DYNAMIC BAYESIAN NETWORK; RANDOMIZED CONTROLLED-TRIALS; EXTERNAL VALIDITY; GRANGER CAUSALITY; TIME-SERIES; DIAGNOSIS; MEDICINE; MODELS; HEALTH;
D O I
10.1016/j.jbi.2011.07.001
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:1102 / 1112
页数:11
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