以数据调查学习原因:问题和方法 (A Survey of Learning Causality with Data: Problems and Methods)

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.

翻译：这项工作考虑了如何方便地获取大量数据如何影响我们了解因果关系和关系的能力的问题。在海量数据时代,学习因果关系与传统数据不同,或与传统数据不同?为了回答这个问题,本调查对在学习因果关系和因果关系以及因果关系和机器学习之间的联系方面的传统和前沿方法进行了全面和有条不紊的审查。这项工作逐个地指出海量数据如何促进、复杂或激励每一种方法。