Learning causality and causality-related learning:some recent progress

被引：6

作者：

Kun Zhang

Bernhard Scholkopf

Peter Spirtes

Clark Glymour

机构：

[1] Department of Philosophy,Carnegie Mellon University

[2] Max Planck Institute for Intelligent Systems

来源：

National Science Review | 2018年 / 5卷 / 01期

基金：

美国国家卫生研究院;

关键词：

Learning causality and causality-related learning:some recent progress; FCI;

D O I：

暂无

中图分类号：

F224 [经济数学方法];

学科分类号：

0701 ; 070104 ;

摘要：

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

引用

页码：26 / 29

页数：4

共 16 条

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[10] Causal discovery from nonstationary/heterogeneous data:Skeleton estimation and orientation determination. Zhang K,Huang B,Zhang J et al. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) . 2017

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