The machine learning community’s interest in causality has significantly increased in recent years. My understanding of causality has been shaped by Judea Pearl and a number of collaborators and colleagues, and much of it went into a book writ ten with Dominik Janzing and Jonas Peters [Peters et al. 2017]. I have spoken about this topic on various occasions, 1 and some of it is in the process of entering the machine learning mainstream, in particular the view that causal modeling can lead to more invariant or robust models. There is excitement about developments at the interface of causality and machine learning, and the present article tries to put my thoughts into writing and draw a bigger picture. I hope it may not only be useful by discussing the importance of causal thinking for AI, but it can also serve as an introduction to some relevant concepts of graphical or structural causal models (SCMs) for a machine learning audience.

1. For example, Schölkopf [2017], talks at the International Conference on Learning Represen tations, the Asian Conference on Machine Learning, and in machine learning labs that have meanwhile developed an interest in causality (eg, DeepMind); much of the present paper is essentially a written-out version of these talks.