星野 崇宏 (ホシノ タカヒロ)

Hoshino, Takahiro

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

学位 【 表示 / 非表示

  • 博士(経済学), 名古屋大学

  • 博士(学術), 東京大学

 

研究分野 【 表示 / 非表示

  • 情報通信 / 統計科学

  • 人文・社会 / 経済統計

  • 人文・社会 / 商学

研究キーワード 【 表示 / 非表示

  • decision making

  • Causal Inference

  • consumer behavior

  • marketing science

  • Behavioral economics

全件表示 >>

 

著書 【 表示 / 非表示

論文 【 表示 / 非表示

  • Semiparametric Bayesian Estimation for Marginal Parametric Potential Outcome Modeling: Application to Causal Inference

    Takahiro Hoshino

    Journal of the American Statistical Association (Journal of the American Statistical Association)  108 ( 504 ) 1189 - 1204 2013年12月

    研究論文(学術雑誌), 単著, 査読有り,  ISSN  01621459

     概要を見る

    We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on potential outcomes. The model uses the probit stick-breaking process mixture proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no parametric model assumption for the assignment model and conditional distribution of the covariate vector. The proposed estimation method is more robust than maximum likelihood estimation, in that it does not require knowledge of the full joint distribution of potential outcomes, covariates, and assignments. In addition, the method is more efficient than fully nonparametric Bayes methods. We apply this model to infer the differential effects of cognitive and noncognitive skills on the wages of production and nonproduction workers using panel data from the National Longitudinal Survey of Youth in 1979. The study also presents the causal effect of online word-of-mouth onWeb site browsing behavior. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  • Real-Time Survey of Vaccine Safety of the mRNA-1273 SARS-CoV-2 Vaccine in Workplace Vaccination at Keio University

    Okumura K., Hara A., Inada I., Sugiyama D., Hoshino T., Yakoh T., Yokoyama H., Urushihara H.

    Vaccines (Vaccines)  10 ( 9 ) 1461 2022年09月

    査読有り

     概要を見る

    The mRNA-1273 Moderna COVID-19 vaccine was introduced to combat the COVID-19 global pandemic in 2020. Although the safety of the vaccine has been investigated worldwide, real-world safety data is scarce in Japan. An online, real-time survey of adverse events following immunization (AEFIs) with mRNA-1273 was conducted in the setting of a workplace vaccination program at the School of Pharmacy, Keio University from 26 June 2021, to 11 June 2022. Participants were requested to take four surveys during a seven-day follow-up period after each of the first, second, and third booster doses. The maximum number of responses, from 301 respondents, was obtained on day 0 (vaccination date) for the first dose. 98% of respondents reported local and systemic AEFIs for the second dose on day 1. No noticeable difference in local reactions was seen among the three doses. Females reported more AEFIs than males, and the young group (18–29 years) reported a higher rate than the middle age group (≥30 years) after the first dose. Age and gender differences in rates decreased at the second and third doses. This survey confirmed that the safety profile of mRNA-1273 in a real-world setting was similar to that derived from the clinical trials, and that the agent was well-tolerated.

  • Joint modeling of effects of customer tier program on customer purchase duration and purchase amount

    Nishio K., Hoshino T.

    Journal of Retailing and Consumer Services (Journal of Retailing and Consumer Services)  66 2022年05月

    最終著者, 責任著者, 査読有り,  ISSN  09696989

     概要を見る

    Nowadays, many supermarkets implement a customer tier program to increase their profits because it is expected to raise customers' willingness to purchase by setting thresholds. However, designing an appropriate program is difficult because each customer's heterogeneous purchase behavior is difficult to capture. Therefore, we simultaneously modeled purchase frequency and amount through a marked point process approach while considering program effects and customer characteristics. The results clarified that the points pressure effect was particularly strong among customers who originally visited the store infrequently and had not attained the threshold set up in the customer tier program many times in the past. In addition, we found that the three-tier customer tier program was superior to the two-tier program with respect to the operating income in supermarkets.

  • Estimation of Local Average Treatment Effect by Data Combination

    Kazuhiko Shinoda, Takahiro Hoshino

    Proceedings of the AAAI Conference on Artificial Intelligence 36 2022年

    最終著者, 査読有り

     概要を見る

    It is important to estimate the local average treatment effect (LATE) when
    compliance with a treatment assignment is incomplete. The previously proposed
    methods for LATE estimation required all relevant variables to be jointly
    observed in a single dataset; however, it is sometimes difficult or even
    impossible to collect such data in many real-world problems for technical or
    privacy reasons. We consider a novel problem setting in which LATE, as a
    function of covariates, is nonparametrically identified from the combination of
    separately observed datasets. For estimation, we show that the direct least
    squares method, which was originally developed for estimating the average
    treatment effect under complete compliance, is applicable to our setting.
    However, model selection and hyperparameter tuning for the direct least squares
    estimator can be unstable in practice since it is defined as a solution to the
    minimax problem. We then propose a weighted least squares estimator that
    enables simpler model selection by avoiding the minimax objective formulation.
    Unlike the inverse probability weighted (IPW) estimator, the proposed estimator
    directly uses the pre-estimated weight without inversion, avoiding the problems
    caused by the IPW methods. We demonstrate the effectiveness of our method
    through experiments using synthetic and real-world datasets.

  • Effects of web-based learning for nurses on their care for pregnant women with hiesho: A randomized controlled trial

    Sachiyo Nakamura, Shoko Takeuchi, Takahiro Hoshino, Naoko Okubo, Shigeko Horiuchi

    Japanese Journal of Nursing Science in press 2022年

    査読有り

全件表示 >>

KOARA(リポジトリ)収録論文等 【 表示 / 非表示

総説・解説等 【 表示 / 非表示

全件表示 >>

研究発表 【 表示 / 非表示

  • ポイントプログラムの長期効果:目標勾配仮説は成立するのか

    星野 崇宏

    第6回行動経済学会大会, 

    2012年12月

    口頭発表(一般)

  • Causal Inference for Multilevel Modeling Under Nonrandom Allocation to Level-2 Units: Moderated causal effect as a function of macro-level variables

    星野 崇宏

    IMPS2012, The 77nd Annual and the 18th International Meetings of the Psychometric Society, 

    2012年07月

    口頭発表(一般)

  • Causal Inference Framework for Latent Variable Modeling

    星野 崇宏

    IMPS2011,The 76th Annual and the 17th International Meetings of the Psychometric Society, 

    2012年07月

    口頭発表(招待・特別)

  • プロスペクト理論を考慮した同時購買行動での価格プロモーション戦略

    星野 崇宏

    第90回日本マーケティング・サイエンス学会研究大会, 

    2011年12月

    口頭発表(一般)

  • 欠測データと因果効果の推定

    星野 崇宏

    ICPSR国内利用協議会統計セミナー, 

    2010年09月

    公開講演,セミナー,チュートリアル,講習,講義等

全件表示 >>

競争的研究費の研究課題 【 表示 / 非表示

  • 因果効果を識別する実行可能な研究デザインの探索と推定法の開発

    2022年04月
    -
    2026年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 星野 崇宏, 基盤研究(B), 補助金,  研究代表者

  • 異質性を考慮した因果効果の推定法の開発とその応用

    2018年04月
    -
    2022年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 星野 崇宏, 基盤研究(B), 補助金,  研究代表者

  • 行動ログと調査回答の乖離の理解及び介入法による改善

    2017年06月
    -
    2019年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 星野 崇宏, 挑戦的研究(萌芽), 補助金,  研究代表者

  • 非実験研究での介入効果推定法の総合的研究と実用化

    2014年04月
    -
    2018年03月

    文部科学省・日本学術振興会, 科学研究費助成事業, 星野 崇宏, 基盤研究(B), 補助金,  研究代表者

受賞 【 表示 / 非表示

  • 日本学術振興会賞

    2017年02月, 日本学術振興会, 日本学術振興会賞

    受賞区分: 国内外の国際的学術賞

  • 義塾賞

    2018年11月, 慶應義塾大学

    受賞区分: 塾内表彰等

  • 日本統計学会 研究業績賞

    2017年09月, 日本統計学会, 日本統計学会 研究業績賞

    受賞区分: 国内学会・会議・シンポジウム等の賞

  • 慶應義塾大学 印東太郎賞

    2012年09月, 慶應義塾大学, 慶應義塾大学 印東太郎賞

    受賞区分: その他

  • 日本行動計量学会 出版賞 調査観察データの統計科学

    2011年09月, 日本行動計量学会, 日本行動計量学会 出版賞 調査観察データの統計科学

    受賞区分: 国内学会・会議・シンポジウム等の賞

全件表示 >>

 

担当授業科目 【 表示 / 非表示

  • 計量経済学演習

    2022年度

  • 研究会d

    2022年度

  • 研究会c

    2022年度

  • 研究会b

    2022年度

  • 研究会a

    2022年度

全件表示 >>

 

所属学協会 【 表示 / 非表示

  • 日本統計学会

     
  • 行動経済学会

     
  • 日本経済学会

     
  • 日本心理学会

     
  • 日本教育心理学会

     

全件表示 >>