From Statistical to Causal Learning

Abstract

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.

To appear in the Proceedings of the International Congress of Mathematicians 2022, EMS Press. Both authors contributed equally to this work; names are listed in alphabetical order. 34 pages (28 content pages + references), 12 figures, 2 tables. arXiv admin note: text overlap with arXiv:1911.10500