岡　達志 （オカ　タツシ）

Oka, Tatsushi

写真a

所属（所属キャンパス）

経済学部 （三田）

職名

教授

HP

外部リンク
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経歴 【 表示 ／ 非表示 】

  • 2023年04月
    -
    継続中

    慶應義塾大学,  経済学部

  • 2022年06月
    -
    2023年03月

    AI Lab CyberAgent

  • 2017年06月
    -
    2022年04月

    モナッシュ大学,  計量経済学部

  • 2010年07月
    -
    2017年05月

    シンガポール国立大学,  経済学部

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学位 【 表示 ／ 非表示 】

  • 経済学博士, ボストン大学, 2010年05月

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研究分野 【 表示 ／ 非表示 】

  • 人文・社会 / 経済統計 （計量経済学）

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論文 【 表示 ／ 非表示 】

  • 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

  • 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.

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

    Xu R., Gao J., Oka T., Whang Y.

    Econometric Reviews 2024年

    ISSN  07474938

    　概要を見る

    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.

  • 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.

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競争的研究費の研究課題 【 表示 ／ 非表示 】

  • 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

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担当授業科目 【 表示 ／ 非表示 】

  • 計量経済学演習

    2025年度

  • 研究会ｄ

    2025年度

  • 研究会ｃ

    2025年度

  • 研究会ｂ

    2025年度

  • 研究会ａ

    2025年度

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