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About Ashesh
Ashesh Rambachan is the Silverman (1968) Family Career Development Assistant Professor of Economics in the Department of Economics at the Massachusetts Institute of Technology, a position he has held since 2025, following his initial appointment as Assistant Professor in 2023. Prior to joining MIT, he was a Postdoctoral Researcher at Microsoft Research New England from 2022 to 2023. Rambachan earned his Ph.D. in Economics from Harvard University in 2022 and his A.B. in Economics, summa cum laude, from Princeton University in 2017. For the 2025-2026 academic year, he is on leave as a Visiting Fellow at the Stanford Institute for Economic Policy Research and Stanford's Economics Department. He also serves as a Research Affiliate at Blueprint Labs, the Learning Collider, and the Center for Applied Artificial Intelligence.
Rambachan's research interests are primarily in econometrics, with a focus on applications of machine learning in economics and causal inference. His publications include "Identifying Prediction Mistakes in Observational Data" in The Quarterly Journal of Economics (2024), "A More Credible Approach to Parallel Trends" with Jonathan Roth in The Review of Economic Studies (2023), "Structural Estimation Under Misspecification: Theory and Implications for Practice" with Isaiah Andrews, Nano Barahona, Matthew Gentzkow, and Jesse Shapiro in The Quarterly Journal of Economics (2025), "Design-based Uncertainty in Quasi-Experiments" with Jonathan Roth forthcoming in The Journal of the American Statistical Association, "Large Language Models: An Applied Econometric Perspective" with Jens Ludwig and Sendhil Mullainathan forthcoming in Annual Review of Economics, and "Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective" with Iavor Bojinov and Neil Shephard in Quantitative Economics (2021). He has received the David A. Wells Prize for the best dissertation in Economics from Harvard (2022), Review of Economic Studies European Tour (2022), China Star Tour (2022), and National Science Foundation Graduate Research Fellowship (2017-2020). Recent grants include National Science Foundation funding for "Comparing Decision Makers: Theory and Implications for AI" (2025-2028) and MIT Generative AI Impact Consortium support (2025-2026). Rambachan referees for journals such as Econometrica, The Quarterly Journal of Economics, and The Review of Economic Studies, and organizes the Machine Learning in Economics Summer Institute (2022-2026).