Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
J Biomed Inform. 2011 Dec;44(6):1102-12. doi: 10.1016/j.jbi.2011.07.001. Epub 2011 Jul 14.
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.
因果关系是整个健康科学领域的一个重要概念，对于使用电子健康记录查找药物不良反应或疾病风险因素等信息学工作尤为重要。虽然哲学家和科学家们几个世纪以来一直在努力形式化什么是原因，但新的推理方法表明，在许多实际情况下，我们可以在这个领域取得进展。本文回顾了理解和识别因果关系的核心概念，然后回顾了当前用于推理和解释的计算方法，重点是从大规模观察数据中进行推理。虽然这个问题还没有完全解决，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。
