Barbara Dick
Assistant Vice President, Alumni Relations
University of Toronto

invites University of Toronto alumni and friends to attend a reception, featuring a special presentation on

From Correlation to Causation: How AI Is Learning to Ask “What If?”

by

Professor Rahul G. Krishnan
Assistant Professor
Department of Computer Science and Department of Laboratory Medicine and Pathobiology
University of Toronto
Tier II Canada Research Chair in Computational Medicine
CIFAR AI Chair at Vector Institute, University of Toronto
Google Scholar

We’re surrounded by data — from health records to economic trends — and it’s easy to spot patterns. But figuring out what causes what is much harder. For example, did a new medication actually help patients recover faster, or were they already on the mend? Did a policy change really improve housing affordability, or was it just a coincidence?

This talk introduces a new kind of artificial intelligence called CausalPFNs, which helps answer these kinds of “what if” questions. Instead of relying solely on human experts to build models, these AI systems learn from simulated worlds where cause and effect are already known. They then apply that knowledge to real-world problems — helping us make better decisions in healthcare, public policy, and everyday life. It’s a step toward AI that doesn’t just find patterns but helps us understand what truly works — and why.

ABOUT THE SPEAKER

Rahul G. Krishnan is an Assistant Professor of Computer Science and Medicine (Laboratory Medicine and Pathobiology) where he holds a Tier II Canada Research Chair in Computational Medicine. He is a CIFAR AI Chair at the Vector Institute and a member of the Temerty Center for Artificial Intelligence in Medicine. He works on deep learning, causal inference and machine learning in healthcare. Previously, he was a Senior Researcher at Microsoft Research New England. He received his MS from New York University and his PhD in Electrical Engineering and Computer Science from MIT in 2020.