Elias Bareinboim

Tuesday, November 3, 2026
4:00 - 5:00 pm (refreshments at 3:30 pm)
Broad Institute Auditorium (415 Main St.)‍

Or via YouTube Livestream: broad.io/ewsc

Everyone is welcome!

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Abstract: While a significant portion of AI scientists and engineers believe we are on the verge of achieving highly general forms of AI, and may have cracked the definition of a "world model," I provide an alternative view through a causal lens. In particular, building on foundational developments in the field, I will present my perspective on the relationship between intelligence and causality, and the central role of the latter in building world models and advancing credible data science, including in the life sciences.

I propose to characterize a causal world model by five core capabilities that we should expect a system equipped with one to exhibit: performing causal reasoning and articulating explanations; making precise, surgical, and sample-efficient decisions; generalizing across changing conditions and environments; generating and simulating in a causally consistent manner; and learning the underlying causal invariances and variables.

In this talk, I will elaborate on this perspective and share current progress toward building intelligent systems that leverage their causal world models and exhibit these five capabilities. A more detailed discussion of this thesis is provided in my forthcoming textbook (MIT Press), a draft of which is available here: https://causalai-book.net

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Biography: Elias Bareinboim is a professor in the Department of Computer Science at Columbia University and the director of the Causal Artificial Intelligence (CausalAI) Laboratory. His research develops causality as a foundation for artificial intelligence, with contributions to causal and counterfactual reasoning, data fusion, learning, generalizability, and decision-making, along with applications in biomedical and social domains. His honors include AAAI Fellow recognition, IEEE "AI's 10 to Watch," and young-investigator awards from NSF, DARPA, and ONR. Bareinboim serves as editor-in-chief of the Journal of Causal Inference and as an action editor of the Journal of Machine Learning Research.

This colloquium is part of a series hosted jointly by the Eric and Wendy Schmidt Center at the Broad Institute and the Department of Electrical Engineering and Computer Science at MIT.

Questions? Email ericandwendyschmidtcenter@broadinstitute.org.