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Designing targeting policies using machine learning

Research Project

Project/Area Number 22K20155
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeMulti-year Fund
Review Section 0107:Economics, business administration, and related fields
Research InstitutionThe University of Tokyo

Principal Investigator

Sakaguchi Shosei  東京大学, 大学院経済学研究科(経済学部), 講師 (30965942)

Project Period (FY) 2022-08-31 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Keywords計量経済学 / ターゲティング政策 / 機械学習
Outline of Research at the Start

個人の特性に応じて適切な政策プログラムを選択して与えるターゲティング政策により、公共政策の社会的効果を増大させることができる。本研究では、機械学習的手法を用いて、最適なターゲティング政策をデータから学習する手法を開発する。本研究では特に、公平性や予算といった公共政策における一般的な制約を満たすターゲティング政策を大規模データから学習するためのアルゴリズムの開発を目的とする。同時に分類問題における既存の機械学習的手法の公共政策のターゲティング学習への応用可能性を明らかにする。

Outline of Final Research Achievements

In this study, I achieved results in both theoretical and empirical aspects regarding the learning of targeting policies in public policy. On the theoretical front, we developed a novel approach to learn optimal dynamic targeting regimes from observational data. The developed method has a double-robustness property and is computationally efficient. I demonstrate that the developed algorithm possesses superior theoretical performance compared to existing methods.

In the empirical study, we estimated and evaluated the optimal targeting policy in a rebate program aimed at reducing household electricity consumption using social experimental data from Japan. Specifically, we demonstrate that the targeting policy can significantly improve social welfare compared to conventional non-targeting policies, suggesting the effectiveness of targeting in public policy.

Academic Significance and Societal Importance of the Research Achievements

データに基づいて政策を個別にデザインするターゲティング政策は、すべての人々に一律・均一に性悪介入する従来型の政策よりも高い社会厚生を実現することが期待される。しかし、データからターゲティング政策をどのように構築するかについては、正確性や計算効率性の点で未だ問題が多かった。本研究では、動的なターゲティング政策について、従来の方法よりも正確性や計算効率性の点で優れた手法を開発した。
また、日本で行った電力消費削減を目指したリベートプログラムの社会実験データを使い、ターゲティング政策が従来の非ターゲティング政策よりも社会厚生を大きく改善することを定量的に示し、ターゲティング政策の有用性を提示した。

Report

(3 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • Research Products

    (24 results)

All 2024 2023 2022 Other

All Int'l Joint Research (6 results) Journal Article (3 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results,  Open Access: 3 results) Presentation (15 results) (of which Int'l Joint Research: 9 results,  Invited: 7 results)

  • [Int'l Joint Research] ブラウン大学/シカゴ大学(米国)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] ユニバーシティ・カレッジ・ロンドン(英国)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] ジュネーブ大学(スイス)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] ブラウン大学/シカゴ大学(米国)

    • Related Report
      2022 Research-status Report
  • [Int'l Joint Research] University College London(英国)

    • Related Report
      2022 Research-status Report
  • [Int'l Joint Research] ジュネーブ大学(スイス)

    • Related Report
      2022 Research-status Report
  • [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

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access
  • [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

    • Related Report
      2022 Research-status Report
    • Open Access / Int'l Joint Research
  • [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

    • Related Report
      2022 Research-status Report
    • Open Access
  • [Presentation] Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data2024

    • Author(s)
      坂口翔政
    • Organizer
      Spring Econometrics Forum
    • Related Report
      2023 Annual Research Report
  • [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
    • Related Report
      2023 Annual Research Report
  • [Presentation] Constrained Classification and Policy Learning2023

    • Author(s)
      坂口翔政
    • Organizer
      CUHK Econometrics Workshop
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research / Invited
  • [Presentation] Sequential Learning of Optimal Dynamic Treatment Regimes with Observational Data2023

    • Author(s)
      坂口翔政
    • Organizer
      Summer Econometrics Forum
    • Related Report
      2023 Annual Research Report
  • [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
    • Related Report
      2023 Annual Research Report 2022 Research-status Report
    • Int'l Joint Research
  • [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
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research / Invited
  • [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
    • Related Report
      2022 Research-status Report
  • [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
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [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
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [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
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [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)
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research / Invited
  • [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
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research / Invited
  • [Presentation] Constrained Classification and Policy Learning2022

    • Author(s)
      坂口翔政
    • Organizer
      Cemmap/SNU Workshop Advances in Econometrics
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research / Invited
  • [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
    • Related Report
      2022 Research-status Report
    • Invited
  • [Presentation] Constrained Classification and Policy Learning2022

    • Author(s)
      坂口翔政
    • Organizer
      東北大学Data Science Workshop
    • Related Report
      2022 Research-status Report
    • Invited

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Published: 2022-09-01   Modified: 2025-01-30  

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