Neural Causal Models | Stefan Bauer

Abstract
Deep neural networks have achieved outstanding success in many tasks ranging from computer vision, to natural language processing, and robotics. However such models still pale in their ability to understand the world around us, as well as generalizing and adapting to new tasks or environments. One possible solution to this problem are causal models, since they can reason about the connections between causal variables and the effect of intervening on them. However, existing algorithms for learning causal graphs from data are often having exponential cost both with the number of variables or the number of observations. This talk will introduce the fundamental concepts of causal inference, connections and synergies with deep learning as well as practical applications and advances in sustainability and AI for science.
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