A Survey of Learning Causality with Data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 22, 2020
- Source ID
- 10.1145/3397269
Entities
People
- Huan Liu
- Jundong Li
- Lu Cheng
- P. Richard Hahn
- Ruocheng Guo
Organizations
- Arizona State University
- National Science Foundation
- United States Army Research Laboratory
- University of Virginia