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.
@article{arxiv.1809.09337,
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},
journal= {arXiv preprint arXiv:1809.09337},
year = {2020}
}