A Survey of Learning Causality with Data: Problems and Methods

TL;DR

This survey reviews how the availability of large datasets influences methods for learning causal relationships, comparing traditional and modern approaches and highlighting the role of big data in causality research.

Contribution

It provides a comprehensive, structured overview of traditional and frontier causality learning methods, emphasizing the impact of big data on these approaches.

Findings

Big data facilitates causal inference in some cases.

Big data complicates causal analysis in others.

Connections between causality and machine learning are extensively discussed.

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.