Causal inference is a branch of machine learning and statistics that aims to develop theoretical models and practical algorithms to infer the statistical causal dynamics in complex systems. The incorporation of causality in learning is what predominantly sets human judgment apart from machines. In this talk, I will briefly explain new developments in causal discovery, the problem of identifiability and inverse aka experimental design, causal transfer learning, and Granger’s notion of causality in time series and discuss how his formulation can go beyond linear dynamics. As an application, I will present an application of causality in imitation learning and developing self-driving vehicles.