Elias Bareinboim

Adjunct Assistant Professor of Computer Science

Joined department: Fall 2015

Education

Ph.D., University of California, Los Angeles, Computer Science (2014)

Professor Barenboim's research focuses on causal and counterfactual inference and their applications to data-driven fields (e.g., medicine, economics, cognitive science). Bareinboim is broadly interested in artificial intelligence, machine learning, statistics, robotics, and the philosophy of science. His thesis work was the first to propose a general solution to the problem of "data fusion," and provides practical methods for combining datasets generated under different experimental conditions. Bareinboim recognitions include the AI's 10 to Watch (IEEE), the Dan David Prize Scholarship, the Yahoo! Key Scientific Challenges Award, and the 2014 AAAI Outstanding Paper Award.

Selected Publications

Causal inference and the data-fusion problem. E. Bareinboim, J. Pearl. Proceedings of the National Academy of Sciences, v. 113 (27), pp. 7345-7352, 2016.

Bandits with Unobserved Confounders: A Causal Approach. E. Bareinboim, A. Forney, J. Pearl. In Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS), 2015.

Transportability from Multiple Environments with Limited Experiments: Completeness Results. E. Bareinboim, J. Pearl. In Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS), 2014.

Recovering from Selection Bias in Causal and Statistical Inference. E. Bareinboim, J. Tian, J. Pearl. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014.

A General Algorithm for Deciding Transportability of Experimental Results. E. Bareinboim, J. Pearl. Journal of Causal Inference, v. 1(1), pp. 107--134, 2013.

Last Updated: Mar 1, 2024 11:27 AM