IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning

Causality is a fundamental notion in science and engineering. It has attracted much interest across research communities in statistics, machine learning (ML), healthcare, and artificial intelligence (AI), and is becoming increasingly recognized as a vital research area. One of the fundamental problems in causality is how to find the causal structure or the underlying causal model. Accordingly, one focus of this Special Issue is on causal discovery, i.e., how can we discover causal structure over a set of variables from observational data with automated procedures? Besides learning causality, another focus is on using causality to help understand and advance ML, that is, causality-inspired ML.