Oka, Tatsushi



Faculty of Economics (Mita)



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Career 【 Display / hide

  • 2023.04

    Keio University,  Department of Economics

  • 2022.06

    AI Lab CyberAgent

  • 2017.06

    Monash University,  Department of Econometrics

  • 2010.07

    National University of Singapore,  Department of Economics

Academic Degrees 【 Display / hide

  • PhD, Boston University, 2010.05


Research Areas 【 Display / hide

  • Humanities & Social Sciences / Economic statistics (Econometrics)


Papers 【 Display / hide

  • Safe Collaborative Filtering

    Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura


     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.

  • Semiparametric Single-Index Estimation for Average Treatment Effects

    Difang Huang, Jiti Gao, Tatsushi Oka


     View Summary

    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.

  • Bivariate Distribution Regression with Application to Insurance Data

    Wang, Y. and Oka, T. and Zhu, D.

    arXiv  2022

    ISSN  23318422

     View Summary

    Understanding variable dependence, particularly eliciting their statistical
    properties given a set of covariates, provides the mathematical foundation in
    practical operations management such as risk analysis and decision making given
    observed circumstances. This article presents an estimation method for modeling
    the conditional joint distribution of bivariate outcomes based on the
    distribution regression and factorization methods. This method is considered
    semiparametric in that it allows for flexible modeling of both the marginal and
    joint distributions conditional on covariates without imposing global
    parametric assumptions across the entire distribution. In contrast to existing
    parametric approaches, our method can accommodate discrete, continuous, or
    mixed variables, and provides a simple yet effective way to capture
    distributional dependence structures between bivariate outcomes and covariates.
    Various simulation results confirm that our method can perform similarly or
    better in finite samples compared to the alternative methods. In an application
    to the study of a motor third-part liability insurance portfolio, the proposed
    method effectively estimates risk measures such as the conditional
    Value-at-Risks and Expexted Sortfall. This result suggests that this
    semiparametric approach can serve as an alternative in insurance risk

  • The effect of human mobility restrictions on the COVID-19 transmission network in China

    Tatsushi Oka, Wei Wei, Dan Zhu

    PLoS ONE (PLoS ONE)  16 ( 7 July 2021 )  2021.07

     View Summary

    Background COVID-19 poses a severe threat worldwide. This study analyzes its propagation and evaluates statistically the effect of mobility restriction policies on the spread of the disease. Methods We apply a variation of the stochastic Susceptible-Infectious-Recovered model to describe the temporal-spatial evolution of the disease across 33 provincial regions in China, where the disease was first identified. We employ Bayesian Markov Chain Monte-Carlo methods to estimate the model and to characterize a dynamic transmission network, which enables us to evaluate the effectiveness of various local and national policies. Results The spread of the disease in China was predominantly driven by community transmission within regions, which dropped substantially after local governments imposed various lockdown policies. Further, Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020. The transmission from these epicenters substantially declined following the introduction of mobility restrictions across regions. Conclusions The spatial transmission network is able to differentiate the effect of the local lockdown policies and the cross-region mobility restrictions. We conclude that both are important policy tools for curbing the disease transmission. The coordination between central and local governments is important in suppressing the spread of infectious diseases.

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

  • Estimation of Multivariate Distribution for Time Series Data


    研究活動スタート支援, Principal investigator