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.

中文翻译：

---

数据学习因果关系调查

这项工作考虑了对大量数据的便捷访问如何影响我们学习因果关系和关系的能力的问题。大数据时代的因果学习与传统学习有哪些不同或相同之处？为了回答这个问题，本调查对学习因果关系和关系的传统方法和前沿方法以及因果关系和机器学习之间的联系进行了全面和结构化的回顾。这项工作逐案指出大数据如何促进、复杂化或激发每种方法。