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.

Bareinboim's work has helped shape the modern causal AI agenda, connecting formal causal inference with core challenges in machine learning and intelligent decision-making. His contributions include the first general solution to the problem of data fusion, providing methods for combining heterogeneous data collected under different experimental conditions and subject to different sources of bias.

His honors include AAAI Fellow recognition, IEEE "AI's 10 to Watch," the NSF CAREER Award, the DARPA Young Faculty Award, the ONR Young Investigator Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award. Bareinboim serves as editor-in-chief of the Journal of Causal Inference and as an action editor of the Journal of Machine Learning Research. He received his Ph.D. from the University of California, Los Angeles, where he was advised by Judea Pearl. His research has been supported by NSF, ONR, AFOSR, DARPA, DoE, NIH, Amazon, JP Morgan, and the Alfred P. Sloan Foundation.