Learning causality and causality-related learning: some recent progress

• 来源期刊: 国家科学评论（英文版）
• 年，卷（期）: 2018,（1）
• 页码范围: 26-29
• 页数: 4页
• 语种: 英文
• DOI: 10.1093/nsr/nwx137

摘要

INTRODUCTION Causality is a fundamental notion in science,and plays an important role in explanation,prediction,decision making and control.Recently,with the rapid accumulation 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 aggregated data from multiple data sets collected in different environments or under different experimental conditions,leading to the issue of data heterogeneity.Causality also provides a way to understand and tackle data heterogeneity,while traditional machine learning typically assumes that the given data follow a fixed distribution.