From Statistical to Causal Learning

Bernhard Schölkopf; Julius von Kügelgen

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arXiv · 2204.00607

From Statistical to Causal Learning

Bernhard Schölkopf·Julius von Kügelgen

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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.

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cs.AI cs.LG stat.ML

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Bernhard Schölkopf, Julius von Kügelgen. 2022-04-01. From Statistical to Causal Learning. https://arxiv.org/abs/2204.00607

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