Quantile Treatment Effects estimated using Causal Machine Learning: Theory and Empirics

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K01593
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 07030:Economic statistics-related
• Research Institution: Senshu University (2022-2023); Tokyo International University (2020-2021)
• Principal Investigator: Chen Jauer 専修大学, 経済学部, 准教授 (70837757)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: 因果的機械学習 / 分位点処置効果 / 計量経済学

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.

Free Research Field

計量経済学，操作変数分位点モデル，機械学習を用いた因果推論

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。