Work

A Survey of Learning Causality with Data

Year: 2020

Type: article

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.

Source: ACM Computing Surveys

DOI: 10.1145/3397269

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

Institutions: Arizona State University, University of Virginia

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Army Aeromedical Research Laboratory

Awards: III: Small: Discovering and Characterizing Implicit Links in Graph Data, III: Small: pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems