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《工程(英文)》 >> 2020年 第6卷 第3期 doi: 10.1016/j.eng.2019.08.016

因果推理

a College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
b Department of Computer Science and Technology, HeFei University of Technology, Hefei 230009, China
c School of Mathematical Science, Peking University, Beijing 100871, China
d Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
e Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
f School of Humanities, Zhejiang University, Hangzhou 310058, China
g University of California Berkeley, Berkeley, CA 94720, USA
h Guanghua School of Management, Peking University, Beijing 100871, China
i Department of Government and Department of Statistics, Harvard University, Cambridge, MA 02138, USA

收稿日期: 2019-05-08 修回日期: 2019-07-21 录用日期: 2019-08-26 发布日期: 2020-01-08

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摘要

因果推理是解释性分析的强大建模工具,它可使当前的机器学习变得可解释。如何将因果推理与机器学习相结合,开发可解释人工智能(XAI)算法,是迈向人工智能2.0的关键步骤之一。为了将因果推理的知识带给机器学习和人工智能领域的学者,我们邀请从事因果推理的研究人员,从因果推理的不同方面撰写了本综述。本综述包括以下几个部分:况琨博士的“平均因果效应评估——简要回顾与展望”,李廉教授的“反事实推理的归因问题”,耿直教授的“Yule-Simpson悖论和替代指标悖论”,徐雷教授的“因果发现CPT方法”,张坤教授的“从观测数据中发现因果关系”,廖备水和黄华新教授的“形式论辩在因果推理和解释中的作用”,丁鹏教授的“复杂实验中的因果推断”,苗旺教授的“观察性研究中的工具变量和阴性对照方法”,蒋智超博士的“有干扰下的因果推断”。

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