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Cited by 366Bayesian Modeling and Causal InferenceAdvanced Causal Inference TechniquesMachine Learning and Algorithms

A Survey of Learning Causality with Data: Problems and Methods

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Guo, Ruocheng, Cheng, Lu, Li, Jundong, Hahn, P. Richard, Liu, Huan·2021·ACM Computing Surveys
DOI 10.1145/3397269
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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.

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Related

Authors
Guo, R., Cheng, L., Li, J., Hahn, P. R., & Liu, H.
Affiliations
  • Arizona State University (US)
  • University of Virginia (US)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2020-07-22
Volume
53
Issue
4
Pages
1-37
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3397269
OpenAlex
W3121171074
Cited by
362
References
100
Citations by year
2026: 13 · 2025: 58 · 2024: 62 · 2023: 74 · 2022: 57 · 2021: 65 · 2020: 25 · 2019: 8
FWCI
21.70
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Army Research Laboratory (NA)
  • NSF (1909555)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
causal discovery, causal inference, big data, causal effect estimation, causal relationship, data availability

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