Information-theoretic Causal Inference And Discovery

This article discusses the importance of causal inference in artificial intelligence and machine learning, and how information theory can be used to improve causal reasoning algorithms. It also highlights some successful applications of information theory in causality research, such as using directed information and the information bottleneck principle. The article calls for submissions for a special issue on the topic, with a focus on introducing new assumptions and addressing causal questions using information-theoretic approaches.