Eli Ben-Michael
Assistant Professor, Heinz College and Department of Statistics & Data Science, Carnegie Mellon University
I am an assistant professor in the Department of Statistics & Data Science and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. I am also affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC). Previously, I was a postdoctoral fellow in the Institute for Quantitative Social Science and the Department of Statistics at Harvard University. I received my PhD in Statistics from U.C. Berkeley and I spent my undergraduate years at Columbia University where I received a bachelors degree in computer science and statistics.
My research focuses on developing statistical and computational methods to solve practical issues in public policy and social science research. I am particularly interested in bringing together ideas from statistics, optimization, and artificial intelligence to create methods for credible and robust causal inference and data-driven decision making.
Contact
Email: ebenmichael@cmu.edu
Office: 2220 Hamburg Hall
Note on PhD admissions: I work with students in both the Statistics & Data Science PhD Programs and the Heinz College PhD Programs. There is also a joint program between the two. For any of these programs, individual faculty do not recruit and accept individual students; all applicants are considered as a whole and offered admission into the program regardless of which faculty member they eventually plan to work with. If you are interested in working with me, please reach out after the admissions process.
Recent papers
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A Weighting Framework for Clusters as Confounders in Observational Studies
2026
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Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
Statistics in Medicine, 2026
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Learning Who to Treat When Treatment is Missing
Conference on Uncertainty in Artificial Intelligence, 2026
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AI-Assisted Variance Reduction in Randomized Experiments
SIGKDD International Conference on Knowledge Discovery and Data Mining, 2026
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Longitudinal Causal Inference with Selective Eligibility
Annals of Applied Statistics, 2026+