Causality for Machine Learning

TL;DR

This paper explores the connection between causal inference and machine learning, highlighting how understanding causality can address fundamental open problems in AI and ML.

Contribution

It discusses the integration of graphical causal inference into machine learning and AI, emphasizing the importance of causality for advancing the field.

Findings

Causality is crucial for solving core AI problems.

Graphical causal inference offers valuable insights for ML.

The field is increasingly recognizing the importance of causality.

Abstract

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.