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

  -

  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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• 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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• Safe Collaborative Filtering

  Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

  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.
  

• Bivariate Distribution Regression with Application to Insurance Data

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

  arXiv （Insurance: Mathematics and Economics)  113   215 - 232 2022年

  ISSN  23318422

  　概要を見る

  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
  management.
  

  • Access to Document (DOI)

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

  Oka T., Wei W., Zhu D.

  PLoS ONE （PLoS ONE)  16 （ 7 July 2021 ）  2021年07月

  　概要を見る

  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.

  • Access to Document (DOI)

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• Safe Collaborative Filtering

  Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

  CoRR abs/2306.05292 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 personalized
  recommender systems to reduce the risk of losing users with low satisfaction.
  This study introduces a "safe" collaborative filtering method that prioritizes
  recommendation quality for less-satisfied users rather than focusing on the
  average performance. Our approach minimizes 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.
  

  • Access to Document (DOI)

• Estimation of Heterogeneous Treatment Effects Using Quantile Regression with Interactive Fixed Effects

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

   2022年08月

  　概要を見る

  We study the estimation of heterogeneous effects of group-level policies,
  using quantile regression with interactive fixed effects. Our approach can
  identify distributional policy effects, particularly effects on inequality,
  under a type of difference-in-differences assumption. We provide asymptotic
  properties of our estimators and an inferential method. We apply the model 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
  a significant negative impact on the between-inequality but little effect on
  the within-inequality.

• 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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• 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