| Project/Area Number |
20K01593
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 07030:Economic statistics-related
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| Research Institution | Senshu University (2022-2023)
Tokyo International University (2020-2021) |
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Principal Investigator |
Chen Jauer 専修大学, 経済学部, 准教授 (70837757)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 因果的機械学習 / 分位点処置効果 / 計量経済学 / Double Machine Learning / コウザルフォレスト / 因果機械学習 |
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| Outline of Research at the Start |
To estimate causal effects, machine learning (ML) methods require adaptations to exploit the structure of economic problems, or to change the optimization criteria of ML algorithms in an economic policy analysis. This research investigates those adaptations, aka the causal ML in economics.
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| Outline of Final Research Achievements |
In this study, we explored the econometric analysis of quantile treatment effects and their application in economics. Specifically, we focused on using causal machine learning to estimate policy effects and causal parameters, as well as constructing their confidence intervals. This research aims to refine existing machine learning methods for causal inference, thereby enhancing the accuracy and interpretability of estimations in empirical economic research.
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| Academic Significance and Societal Importance of the Research Achievements |
学術的には、因果的機械学習の手法を用いることで、経済データの解釈と活用が向上しました。社会的には、この研究は政策立案者がより根拠に基づいた効果的な経済政策を行うための支援となることを期待しています。特に、政策のターゲティングや効果の評価において、より精確な情報を提供することが可能となりました。
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