From statistical to causal learning
Bernhard Schölkopf
Max Planck Institute for Intelligent Systems, Tübingen, GermanyJulius von Kügelgen
Max Planck Institute for Intelligent Systems, Tübingen, Germany; and University of Cambridge, Cambridge, United Kingdom
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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.