From Bayesian to Causal Networks: Graphical Models in Artificial Intelligence

Iberamia 2024 Career Recognition Award talk

Pr. Luis Enrique Sucar

Senior Research Scientist
Department of Computing

INAOE
Instituto Nacional de Astrofísica, Óptica y Electrónica

Tonantzintla, Puebla

72840 MEXICO

Probabilistic Graphical Models are a set of powerful techniques to represent and reason under uncertainty in artificial intelligence. After presenting an overview of graphical models, I will focus on Bayesian networks and causal models. Bayesian networks provide a compact representation of a joint probability distribution, including efficient methods for probabilistic reasoning and learning. We will cover some inference and learning algorithms, and illustrate their applications in medicine and industry. However, Bayesian networks are limited to represent associations, to go further and reason about interventions and counterfactuals we requiere causal models. I will give an introduction to causal graphical models and how to predict the effects on interventions and imagine alternative scenarios. We will cover causal discovery, that is learning cause-effect relations from data, and some applications in medicine and robotics. Finally, we will see how to combine reinforcement learning with causal discovery, to learn at the same time an optimal policy and a causal model.

Short Bio: L. Enrique Sucar (Senior Member IEEE) has a Ph.D. in Computing from Imperial College, London, 1992; a M.Sc. in Electrical Engineering from Stanford University, USA, 1982; and a B.Sc. in Electronics and Communications Engineering from ITESM, Mexico, 1980. He has been a researcher at the Electrical Research Institute, a professor at ITESM, and is currently Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics, Puebla, Mexico. He has been an invited professor at the University of British Columbia, Canada; Imperial College, London; INRIA, France; and CREATE-NET, Italy. He has more than 400 publications and has directed nearly 100 Ph.D. and M.Sc. thesis. Dr. Sucar received the National Science Prize from the Mexican President; is Member Emeritus of the National Research System, and member of the Mexican Science Academy. He is associate editor of the Pattern Recognition, Computational Intelligence and Frontiers in Rehabilitation journals, and has served as president of the Mexican AI Society and the Mexican Academy of Computing. His main research interest are in probabilistic graphical models, causal reasoning and their applications in robotics, computer vision and biomedicine.