A Survey of Learning Causality with Data: Problems and Methods
It provides a comprehensive review for researchers, but is incremental as it synthesizes existing knowledge without introducing new methods.
This survey examines how the availability of big data affects the learning of causal effects and relations, comparing traditional and modern methods while exploring connections between causality and machine learning.
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.