معرفی
Tetsuya Kaji is an Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research spans econometrics, statistics, and machine learning, focusing on nonstandard econometric problems, empirical processes, and AI adaptation in economic applications.
- Education: Interdisciplinary PhD in Economics and Statistics from MIT; MA in Economics from the University of Tokyo.
Recent work highlights adversarial inference for structural estimation, classification-based sampling (Metropolis-Hastings, ABC methods), and weak identification in semiparametric models. He also explores tail risk control and treatment effect heterogeneity.
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