Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

翻译：Judea Pearl所首创的图形因果推论源于人工智能研究(AI),而且长期以来与机器学习领域几乎没有联系,这篇文章讨论了已经和应该建立联系的领域,在过程中引入了关键概念,认为机器学习和人工智能的硬性开放问题与因果关系有着内在的联系,并解释了该领域如何开始理解这些联系。