A Survey of Learning Causality with Data: Problems and Methods

Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from - or the same as - the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.

Original languageEnglish (US)
Article number3397269
JournalACM Computing Surveys
Volume53
Issue number4
DOIs
StatePublished - Sep 2020

Keywords

  • Causal machine learning
  • causal discovery
  • causal inference

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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