A Survey of Learning Causality with Data: Problems and Methods

Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu
9/25/2018
cs.AIstat.ME

Official code: rguo12/awesome-causality-algorithms (3,292 stars, MIT, last commit 2026-10-07). 1 more implementation below.

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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Code Implementations(2)

rguo12/awesome-causality-algorithmsOfficial100%
MIT

An index of algorithms for learning causality with data

3,292483Sep 19, 20182 weeks agoMIT
awesomebaselinescausal-inferencecausalitycausality-algorithms+5 more
rguo12/awesome-causality-dataOfficial100%
MIT

A data index for learning causality.

49471Aug 22, 20182 years agoMIT

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Cite this paper

@article{guo2018survey,
  title  = {A Survey of Learning Causality with Data: Problems and Methods},
  author = {Ruocheng Guo and Lu Cheng and Jundong Li and P. Richard Hahn and Huan Liu},
  year   = {2018},
  eprint = {1809.09337},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.09337}
}

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