慶應義塾研究者情報データベース

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• 2023年04月

  -

  継続中

  慶應義塾大学,  経済学部

• 2022年06月

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  2023年03月

  AI Lab CyberAgent

• 2017年06月

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  2022年04月

  モナッシュ大学,  計量経済学部

• 2010年07月

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  2017年05月

  シンガポール国立大学,  経済学部

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• 経済学博士, ボストン大学, 2010年05月

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• 人文・社会 / 経済統計 （計量経済学）

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• 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年10月

  • Access to Document (DOI)

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

  Byambadalai U., Oka T., Yasui S.

  Proceedings of Machine Learning Research 235   5082 - 5113 2024年

  　概要を見る

  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.

• Safe Collaborative Filtering

  Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

  12th International Conference on Learning Representations, ICLR 2024 2023年06月

  　概要を見る

  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

  Oka T., Yamada K.

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

  ISSN  0022166X

  　概要を見る

  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)

• Semiparametric Single-Index Estimation for Average Treatment Effects

  Difang Huang, Jiti Gao, Tatsushi Oka

  2022年06月

  　概要を見る

  We propose a semiparametric method to estimate the average treatment effect
  under the assumption of unconfoundedness given observational data. Our
  estimation method alleviates misspecification issues of the propensity score
  function by estimating the single-index link function involved through Hermite
  polynomials. Our approach is computationally tractable and allows for
  moderately large dimension covariates. We provide the large sample properties
  of the estimator and show its validity. Also, the average treatment effect
  estimator achieves the parametric rate and asymptotic normality. Our extensive
  Monte Carlo study shows that the proposed estimator is valid in finite samples.
  We also provide an empirical analysis on the effect of maternal smoking on
  babies' birth weight and the effect of job training program on future earnings.
  

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競争的研究費の研究課題 【 表示 ／ 非表示 】

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• ランダム化実験下での分布効果の推定手法

  2024年04月

  -

  2027年03月

  岡 達志, 基盤研究(C), 補助金,  研究代表者

• 時系列データの多変量分布の推定

  2023年08月

  -

  2025年03月

  岡 達志, 研究活動スタート支援, 補助金,  研究代表者