KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

Career 【 Display / hide 】

• 2023.04

  -

  Present

  Keio University,  Department of Economics

• 2022.06

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  2023.03

  AI Lab CyberAgent

• 2017.06

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  2022.04

  Monash University,  Department of Econometrics

• 2010.07

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  2017.05

  National University of Singapore,  Department of Economics

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• PhD, Boston University, 2010.05

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Humanities & Social Sciences / Economic statistics (Econometrics)

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• Regression adjustment for estimating distributional treatment effects in randomized controlled trials

  Tatsushi Oka, Shota Yasui, Yuta Hayakawa, Undral Byambadalai

  Econometric Reviews 45 ( 1 ) 2 - 17 2026

  ISSN  07474938

  　View Summary

  In this article, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment heterogeneity compared to average treatment effects. We propose a regression adjustment method that utilizes distributional regression and pre-treatment information, establishing theoretical efficiency gains without imposing restrictive distributional assumptions. We develop a practical inferential framework and demonstrate its advantages through extensive simulations. Analyzing water conservation policies, our method reveals that behavioral nudges systematically shift consumption from high to moderate levels. Examining health insurance coverage, we show the treatment reduces the probability of zero doctor visits by 6.6 percentage points while increasing the likelihood of 3-6 visits. In both applications, our regression adjustment method substantially improves precision and identifies treatment effects that were statistically insignificant under conventional approaches.

  • Access to Document (DOI)

• Latent Variable Modeling for Robust Causal Effect Estimation

  Morimura T., Oka T., Suzuki Y., Moriwaki D.

  Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management    2179 - 2189 2025.11

  　View Summary

  Latent variable models provide a powerful framework for incorporating and inferring unobserved factors in observational data. In causal inference, they help account for hidden factors influencing treatment or outcome, thereby addressing challenges posed by missing or unmeasured covariates. This paper proposes a new framework that integrates latent variable modeling into the double machine learning (DML) paradigm to enable robust causal effect estimation in the presence of such hidden factors. We consider two scenarios: one where a latent variable affects only the outcome, and another where it may influence both treatment and outcome. To ensure tractability, we incorporate latent variables only in the second stage of DML, separating representation learning from latent inference. We demonstrate the robustness and effectiveness of our method through extensive experiments on both synthetic and real-world datasets.

  • Access to Document (DOI)

• QR.break: An R Package for Structural Breaks in Quantile Regression

  Qu, Z., Oka, T., Messer, S.

  Journal of Econometric Methods 14 ( 1 ) 21 - 34 2025.01

  ISSN  21566674

  　View Summary

  The QR.break package provides methods for detecting, estimating, and conducting inference on multiple structural breaks in linear quantile regression models, based on one or multiple quantiles and applicable to both time series and repeated cross-sectional data. The main function, rq.break(), returns testing and estimation results based on user specifications of the quantiles of interest, the maximum number of breaks allowed, and the minimum length of a single regime. This note outlines the underlying methods and explains how to use the main function with two datasets: A time series dataset on U.S. real GDP growth rates and a repeated cross-sectional dataset on youth drinking and driving behavior. Both datasets are included in the package available on CRAN.

  • Access to Document (DOI)

• On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization

  Byambadalai U., Hirata T., Oka T., Yasui S.

  Proceedings of Machine Learning Research 267 2025

  　View Summary

  This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron’s biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and empirical analyses of microcredit programs highlight the practical advantages of our method.

• Exploring Explanations Improves the Robustness of In-Context Learning

  Honda U., Oka T.

  Proceedings of the Annual Meeting of the Association for Computational Linguistics 1   23693 - 23714 2025

  ISSN  0736587X

  　View Summary

  In-context learning (ICL) has emerged as a successful paradigm for leveraging large language models (LLMs). However, it often struggles to generalize beyond the distribution of the provided demonstrations. A recent advancement in enhancing robustness is ICL with explanations (X-ICL), which improves prediction reliability by guiding LLMs to understand and articulate the reasoning behind correct labels. Building on this approach, we introduce an advanced framework that extends X-ICL by systematically exploring explanations for all possible labels (X²-ICL), thereby enabling more comprehensive and robust decision-making. Experimental results on multiple natural language understanding datasets validate the effectiveness of X²-ICL, demonstrating significantly improved robustness to out-of-distribution data compared to the existing ICL approaches.

  • Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

• Development of distribution estimation methods for multivariate time series data and their application to forecasting macroeconomic fluctuations

  Oka, Tatsushi

  学事振興資金研究成果実績報告書 (慶應義塾大学)  2023

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Research Projects of Competitive Funds, etc. 【 Display / hide 】

• ランダム化実験下での分布効果の推定手法

  2024.04

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  2027.03

  基盤研究(C), Principal investigator

• Estimation of Multivariate Distribution for Time Series Data

  2023.08

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  2025.03

  研究活動スタート支援, Principal investigator