A review of causal inference for biomedical informatics.
A review of causal inference for biomedical informatics.
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DOI：
10.1016/j.jbi.2011.07.001
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发表时间：
2011-12
影响因子：
4.5
通讯作者：
Hripcsak, George
中科院分区：
文献类型：
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作者：
Kleinberg, Samantha;Hripcsak, George
因果关系是贯穿健康科学的一个重要概念，对于信息学工作尤其重要，例如使用电子健康记录查找药物不良事件或疾病风险因素。尽管哲学家和科学家花了几个世纪的时间来研究是什么使某件事成为原因还没有达成共识，但新的推理方法表明，在许多实际情况下，我们可以在这一领域取得进展。本文回顾了理解和识别因果关系的核心概念，然后回顾了目前推断和解释的计算方法，重点是大规模观测数据的推断。虽然问题没有完全解决，但我们表明图形模型和格兰杰因果关系为推理提供了有用的框架，并且基于时间逻辑的最新方法解决了这些方法的一些局限性。
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.