Invited talk
in
Workshop: Planning in The Era of Language Models (LM4Plan) Fri, Jul 10, 2026 • 6:45 PM – 7:30 PM PDT

Towards Causal Artificial Intelligence

Elias Bareinboim

Abstract

While a significant portion of AI scientists and engineers believe we are on the verge of achieving highly general forms of AI, I offer a critical appraisal of this 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 intelligent systems and advancing credible data science.

I frame this discussion in terms of five core capabilities that we should expect from an intelligent AI system:

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 causal structures and variables.

In this talk, I will elaborate on this perspective and share current progress toward building causally intelligent AI systems. A more detailed discussion of this thesis is provided in my forthcoming textbook, a draft of which is available here: https://urldefense.proofpoint.com/v2/url?u=https-3A_causalai-2Dbook.net&d=DwIFaQ&c=BSDicqBQBDjDI9RkVyTcHQ&r=QXqf7M6Mt-8N49HP5wCevKGChwr6XhPpzu6x1URGLy8&m=0Jj8a8G0mRUfFehF118-l8TEjRwKMQ8lhLwdJntoXugMiQSZsusB4xbO48c-DBw-&s=28EbzSHK4ygMjqrdyBOU0dTBagWmoQR2T2TEUxzJ1YE&e= .

Speaker

Elias Bareinboim

Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to artificial intelligence and machine learning as well as data-driven fields in the health and social sciences. His work was the first to propose a general solution to the problem of "causal data-fusion," providing practical methods for combining datasets generated under different experimental conditions and plagued with various biases. In the last years, Bareinboim has been exploring the intersection of causal inference with decision-making (including reinforcement learning) and explainability (including fairness analysis). Before joining Columbia, he was an assistant professor at Purdue University and received his Ph.D. in Computer Science from the University of California, Los Angeles. Bareinboim was named one of ``AI's 10 to Watch'' by IEEE, and is a recipient of an NSF CAREER Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award.

Video

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