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2019 Fiscal Year Final Research Report

Inference on causal effects for misclassified treatment

Research Project

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Project/Area Number 17K13715
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Economic statistics
Research InstitutionKyoto University (2018-2019)
Hitotsubashi University (2017)

Principal Investigator

Yanagi Takahide  京都大学, 経済学研究科, 特定講師 (30754832)

Project Period (FY) 2017-04-01 – 2020-03-31
Keywords計量経済学 / ミクロ計量経済学 / 政策評価 / 測定誤差
Outline of Final Research Achievements

In this study, I examined statistical causal inference models in which the causal variable may be misclassified due to the presence of measurement error. I obtained the following results. First, I clarified identification problems caused by the presence of measurement error. Then, I proposed new identification results for local average treatment effects, which is one of the most important causal parameters in the literature, when the causal variable may be misclassified. Based on the identification result, I also proposed estimation procedures for the local average treatment effects. In addition, I studied how to extend these results to other statistical causal inference models.

Free Research Field

計量経済学

Academic Significance and Societal Importance of the Research Achievements

政策の効果を評価するために,経済学の実証研究では局所平均処置効果モデルを利用することが多い.経済学の実証研究ではデータ収集の過程で原因変数にエラーが含まれてる可能性があるが,これまでの多くの実証研究ではそのようなエラーから生じる問題に対処できていなかった.本研究で得られた研究成果を利用すれば,このようなエラーから生じる問題を解決できるとともに,政策の効果を正しく評価できる蓋然性を高めることができるといえる.

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Published: 2021-02-19  

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