Learning causality and causality-related learning:some recent progress

作     者：Kun Zhang Bernhard Scholkopf Peter Spirtes Clark Glymour 

作者机构：Department of PhilosophyCarnegie Mellon University Max Planck Institute for Intelligent Systems 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2018年第5卷第1期

页      面：26-29页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：support from the National Institutes of Health(NIH-1R01EB022858-01 FAINR01EB022858 NIH-1R01LM012087 and NIH-5U54HG008540-02 FAINU54HG008540) 

主　　题：Learning causality and causality-related learning:some recent progress FCI 

摘      要：INTRODUCTION Causality is a fiundamental notion in science,and plays an important role in explanation,prediction,decision making and ***,with the rapidaccumulation of huge volumes of data,it is even more desirable to abstract causal knowledge from ***,such data are usually time series measured over a relatively long time period or ag-