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

[11] Causal discovery in the presence of measurement error:Identifiability conditions. Zhang K,Gong M,Ramsey J et al. UAI 2017 Workshop on Causality:Learning,Inference,and DecisionMaking . 2017
[12] Discovering cyclic causal models by independent components analysis. Lacerda G,Spirtes P,Ramsey J,Hoyer PO. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence . 2008
[13] Transportability of Causal and Statistical Relations: A Formal Approach. J Pearl,E Bareinboim. PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE . 2011
[14] On the identifiability of the post-nonlinear causal model. Zhang K,Hyvarinen A. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence . 2009
[15] On the identifiability and estimation of functional causal models in the presence of outcome-dependent selection. Zhang K,Zhang J,Huang B et al. Proceedings of the 32rd Conference on Uncertainty in Artificial inteiiigence (UAI 2016) OR . 2016
[16] Causal discovery from temporally aggregated time series. Gong M,Zhang K,Scholkopf B et al. Proc.Conference on Uncertainty in Artificial Intelligence (UAI).OR . 2017

← 1 2 →