Naoki Egami

Naoki Egami is an Associate Professor (with tenure) of Political Science at the Massachusetts Institute of Technology. He is also a faculty affiliate of the Institute for Data, Systems, and Society (IDSS) at MIT. Egami specializes in political methodology and develops statistical methods for questions in political science and the social sciences. Specifically, he works on causal inference and machine learning methods. His current research programs focus on three areas: (1) External Validity, (2) Machine Learning and AI for the Social Sciences, and (3) Causal Inference with Network and Spatial Data.

His work has appeared or is forthcoming in various academic journals in political science, statistics, and computer science, such as American Political Science Review, American Journal of Political Science, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B), Neurips, and Science Advances. For his broad methodological contributions, Egami received the Best Paper Awards from three different sections of the American Political Science Association: the Experimental Research section (2024), the Political Networks section (2022), and the Political Methodology section (2019). He also received the John T. Williams Dissertation Prize (2017) from the Society for Political Methodology.

Before joining MIT, Egami was an Assistant Professor at Columbia University from 2020 to 2025. He received a Ph.D. from Princeton University (2020) and a B.A. from the University of Tokyo (2015). He was a visiting graduate student fellow in the Department of Government at Harvard University from 2018 to 2020. He also studied at the University of Michigan as a visiting undergraduate student in 2013.