Tetsuya Kaji

Associate Professor of Econometrics and Statistics
University of Chicago Booth School of Business

CV

I am Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. I work at the intersections of economics, statistics, and machine learning.

Working Papers

• A LATE-Targeting Experimental Design for Multi-Arm RCTs

  Preprint

• Assessing Heterogeneity of Treatment Effects

  with Jianfei Cao

  Preprint

• Controlling Tail Risk Measures with Estimation Error

  with Hyungjune Kang

  Preprint

Publications

• Convergence in Distribution of the P-P Process in \(L^1[0,1]\)

  Stat, 15(4), e70177, December 2026 · with Brendan Beare

  Article Preprint

• A Necessary and Sufficient Condition for Convergence in Distribution of Quantile Process in \(L^1(0,1)\)

  Bernoulli, accepted, 2026 · with Brendan Beare

  Article Preprint

• Why Do the Elderly Save? Using Health Shocks to Uncover Bequest Motives

  Japanese Economic Review, 77(2), 355–378, April 2026 · with Elena Manresa

  Article Preprint

• The Hellinger Bounds on the Kullback-Leibler Divergence and the Bernstein Norm

  Japanese Economic Review, 77(2), 333–354, April 2026

  Article Preprint

• An Adversarial Approach to Structural Estimation

  Econometrica, 91(6), 2041–2063, November 2023 · with Elena Manresa and Guillaume Pouliot

  Article Preprint Supplement Code CBR

• Metropolis-Hastings via Classification

  Journal of the American Statistical Association, 118(544), 2533–2547, 2023 · with Veronika Ročková

  Article Preprint Supplement

• Approximate Bayesian Computation via Classification

  Journal of Machine Learning Research, 23(350), 1–49, 2022 · with Yuexi Wang and Veronika Ročková

  Article Preprint

• Adversarial Inference Is Efficient

  AEA Papers and Proceedings, 111, 621–625, May 2021 · with Elena Manresa and Guillaume Pouliot

  Article Supplement

• Theory of Weak Identification in Semiparametric Models

  Econometrica, 89(2), 733–763, March 2021

  Article Preprint Code

• Extremal Quantile Regression

  Handbook of Quantile Regression, ed. by R. Koenker, V. Chernozhukov, X. He, and L. Peng, Chapman & Hall/CRC, 2017, ch. 18, 333–362 · with Victor Chernozhukov and Iván Fernández-Val

  Chapter Preprint Code