KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

Career 【 Display / hide 】

• 2023.04

  -

  Present

  Keio University,  Department of Economics

• 2022.06

  -

  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 】

• Not Eliminate but Aggregate: Post-Hoc Control over Mixture-of-Experts to Address Shortcut Shifts in Natural Language Understanding

  Honda, U; Oka, T; Zhang, PA; Mita, M

  TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS 12   1268 - 1289 2024.10

  • Access to Document (DOI)

• Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction.

  Undral Byambadalai, Tatsushi Oka, Shota Yasui

  ICML 235   5082 - 5113 2024

  　View Summary

  We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.

• Quantile random-coefficient regression with interactive fixed effects: Heterogeneous group-level policy evaluation

  Ruofan Xu, Jiti Gao, Tatsushi Oka, Yoon–Jae Whang

  Econometric Reviews  2024

  ISSN  07474938

  　View Summary

  We propose a quantile random-coefficient regression with interactive fixed effects to study the effects of group-level policies that are heterogeneous across individuals. Our approach is the first to use a latent factor structure to handle the unobservable heterogeneities in the random coefficient. The asymptotic properties and an inferential method for the policy estimators are established. The model is applied to evaluate the effect of the minimum wage policy on earnings between 1967 and 1980 in the United States. Our results suggest that the minimum wage policy has significant and persistent positive effects on black workers and female workers up to the median. Our results also indicate that the policy helps reduce income disparity up to the median between two groups: black, female workers versus white, male workers. However, the policy is shown to have little effect on narrowing the income gap between low- and high-income workers within the subpopulations.

  • Access to Document (DOI)

• Safe Collaborative Filtering.

  Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

  ICLR  2023.06

  　View Summary

  Excellent tail performance is crucial for modern machine learning tasks, such
  as algorithmic fairness, class imbalance, and risk-sensitive decision making,
  as it ensures the effective handling of challenging samples within a dataset.
  Tail performance is also a vital determinant of success for personalised
  recommender systems to reduce the risk of losing users with low satisfaction.
  This study introduces a "safe" collaborative filtering method that prioritises
  recommendation quality for less-satisfied users rather than focusing on the
  average performance. Our approach minimises the conditional value at risk
  (CVaR), which represents the average risk over the tails of users' loss. To
  overcome computational challenges for web-scale recommender systems, we develop
  a robust yet practical algorithm that extends the most scalable method,
  implicit alternating least squares (iALS). Empirical evaluation on real-world
  datasets demonstrates the excellent tail performance of our approach while
  maintaining competitive computational efficiency.
  

• Heterogeneous Impact of the Minimum Wage Implications for Changes in Betweenand Within-Group Inequality

  Tatsushi Oka, Ken Yamada

  Journal of Human Resources (Journal of Human Resources)  58 ( 1 ) 335 - 362 2023

  ISSN  0022166X

  　View Summary

  In the United States, most of the workers who earn at or below the minimum wage are either less educated, young, or female. We examine the extent to which the minimum wage influences the wage differential among workers with different observed characteristics and the wage differential among workers with the same observed characteristics. Our results suggest that changes in the real value of the minimum wage account in part for the patterns of changes in education, experience, and gender wage differentials and for most of the changes in within-group wage differentials for workers with lower levels of experience.

  • Access to Document (DOI)

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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

  -

  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

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• SEMINAR: ECONOMETRICS

  2025

• RESEARCH SEMINAR D

  2025

• RESEARCH SEMINAR C

  2025

• RESEARCH SEMINAR B

  2025

• RESEARCH SEMINAR A

  2025

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