Document worth reading: “Causality for Machine Learning”

Graphical causal inference as pioneered by Judea Pearl arose from evaluation on artificial intelligence (AI), and for a really very long time had little connection to the sphere of machine learning. This article discusses the place hyperlinks have been and must be established, introducing key concepts alongside one of the best ways. It argues that the onerous open problems with machine learning and AI are intrinsically related to causality, and explains how the sphere is beginning to grasp them. Causality for Machine Learning