Title: Bridging causality and deep learning with causal generative models

Bio: Bryon Aragam is an Associate Professor and Topel Faculty Scholar in the Booth School of Business at the University of Chicago. He studies causality, statistical machine learning, and probabilistic modeling. His current interests involve causal machine learning, deep generative models, latent variable models, and statistical learning theory. In particular, this work focuses on applications of artificial intelligence, including tools such as ChatGPT and DALL-E. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work is supported by the NSF and the NIH. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow.

Abstract: Generative models for vision and language have shown remarkable capacities to emulate creative processes but still lack fundamental skills that have long been recognized as essential for genuinely autonomous intelligence. Difficulties with causal reasoning and concept abstraction highlight critical gaps in current models, despite their nascent capacities for reasoning and planning. Bridging this gap requires a synthesis of deep learning's expressiveness with the powerful framework of statistical causality.

We will discuss our recent efforts towards building generative models that extract causal knowledge from data while retaining the flexibility and expressivity of deep learning. Unlike traditional causal methods that rely on predefined causal structures, we tackle the more complex problem of learning causal structure directly from data--even when the causal variables themselves are not explicitly observed. This introduces significant challenges, including ill-posedness, nonconvexity, and the exponential complexity of combinatorial search. We will outline statistical aspects of these problems and present progress towards resolving these challenges with differentiable approaches to causal discovery and representation learning.