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SAKAGUCHI SHOSEI  坂口 翔政

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Sakaguchi Shosei  坂口 翔政

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Researcher Number 30965942
Affiliation (Current) 2025: 東京大学, 大学院経済学研究科(経済学部), 講師
Affiliation (based on the past Project Information) *help 2022 – 2024: 東京大学, 大学院経済学研究科(経済学部), 講師
Review Section/Research Field
Principal Investigator
Basic Section 07030:Economic statistics-related / 0107:Economics, business administration, and related fields
Keywords
Principal Investigator
機械学習 / ターゲティング政策 / 計量経済学 / 動的トリートメント・レジーム
  • Research Projects

    (2 results)
  • Research Products

    (18 results)
  •  Designing dynamic public policies using machine learning techniquesPrincipal Investigator

    • Principal Investigator
      坂口 翔政
    • Project Period (FY)
      2024 – 2027
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 07030:Economic statistics-related
    • Research Institution
      The University of Tokyo
  •  Designing targeting policies using machine learningPrincipal Investigator

    • Principal Investigator
      Sakaguchi Shosei
    • Project Period (FY)
      2022 – 2023
    • Research Category
      Grant-in-Aid for Research Activity Start-up
    • Review Section
      0107:Economics, business administration, and related fields
    • Research Institution
      The University of Tokyo

All 2024 2023 2022

All Journal Article Presentation

  • [Journal Article] Partial identification and inference in duration models with endogenous censoring2023

    • Author(s)
      Sakaguchi Shosei
    • Journal Title

      Journal of Applied Econometrics

      Volume: 39 Issue: 2 Pages: 308-326

    • DOI

      10.1002/jae.3024

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Journal Article] Collaborative knowledge exchange promotes innovation2022

    • Author(s)
      Tomoya Mori, Jonathan Newton, Shosei Sakaguchi
    • Journal Title

      arXiv

      Volume: arXiv:2210.01392 Pages: 1-4

    • Open Access
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Journal Article] Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs2022

    • Author(s)
      Ida Takanori、Ishihara Takunori、Ito Koichiro、Kido Daido、Kitagawa Toru、Sakaguchi Shosei、Sasaki Shusaku
    • Journal Title

      NBER WORKING PAPER

      Volume: 30469 Pages: 1-47

    • DOI

      10.3386/w30469

    • Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2024

    • Author(s)
      坂口翔政
    • Organizer
      Spring Econometrics Forum
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2024

    • Author(s)
      坂口翔政
    • Organizer
      2023年度関西計量経済学研究会
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Constrained Classification and Policy Learning2023

    • Author(s)
      坂口翔政
    • Organizer
      2022年度関西計量経済学研究会
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs2023

    • Author(s)
      坂口翔政
    • Organizer
      Bravo/JEA/SNSF Workshop on Using Data to Make Decisions
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Sequential Learning of Optimal Dynamic Treatment Regimes with Observational Data2023

    • Author(s)
      坂口翔政
    • Organizer
      Summer Econometrics Forum
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Constrained Classification and Policy Learning2023

    • Author(s)
      坂口翔政
    • Organizer
      CUHK Econometrics Workshop
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Doubly Robust Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2023

    • Author(s)
      坂口翔政
    • Organizer
      2023 Asian Meeting of the Econometric Society
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Constrained Classification and Policy Learning2022

    • Author(s)
      坂口翔政
    • Organizer
      東京大学応用統計ワークショップ
    • Invited
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs2022

    • Author(s)
      坂口翔政
    • Organizer
      2022 North American Summer Meetings of the Econometric Society
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Constrained Classification and Policy Learning2022

    • Author(s)
      坂口翔政
    • Organizer
      東北大学Data Science Workshop
    • Invited
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Constrained Classification and Policy Learning2022

    • Author(s)
      坂口翔政
    • Organizer
      Cemmap/SNU Workshop Advances in Econometrics
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Doubly Robust Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2022

    • Author(s)
      坂口翔政
    • Organizer
      15th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2022)
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Doubly Robust Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2022

    • Author(s)
      坂口翔政
    • Organizer
      2022 Asia Meeting of the Econometric Society, East and South East Asia
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Doubly Robust Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2022

    • Author(s)
      坂口翔政
    • Organizer
      The 16th International Symposium on Econometric Theory and Applications: SETA2022
    • Invited / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155
  • [Presentation] Paternalism, Autonomy, or Both? Experimental Evidence from Energy Saving Programs2022

    • Author(s)
      坂口翔政
    • Organizer
      International Association for Applied Econometrics (IAAE) 2022 Annual Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K20155

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