HETEROGENEOUS CAUSAL EFFECTS WITH IMPERFECT COMPLIANCE: A BAYESIAN MACHINE LEARNING APPROACH

• 成果类型: Article
• 署名作者: Bargagli-Stoffi, Falco J.; de Witte, Kristof; Gnecco, Giorgio
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; KU Leuven; IMT School for Advanced Studies Lucca
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1579
• 发表日期: 2022
• 页码: 1986-2009
• 关键词: false discovery rate; Regression discontinuity designs; rejective multiple test; instrumental variables; university grants; inference; identification; performance; statistics

摘要

This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology outperforms other machine learning techniques tailored for causal inference in discovering and estimating the heterogeneous causal effects while controlling for the familywise error rate (or, less stringently, for the false discovery rate) at leaves' level. BCF-IV sheds a light on the heterogeneity of causal effects in instrumental variable scenarios and, in turn, provides the policy-makers with a relevant tool for targeted policies. Its empirical application evaluates the effects of additional funding on students' performances. The results indicate that BCF-IV could be used to enhance the effectiveness of school funding on students' performance.