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

F.J. Bargagli-Stoffi*, K. DE WITTE, G. Gnecco

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

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.
Original languageEnglish
Pages (from-to)1986-2009
Number of pages24
JournalAnnals of Applied Statistics
Volume16
Issue number3
DOIs
Publication statusPublished - Sept 2022

JEL classifications

  • c10 - Econometric and Statistical Methods and Methodology: General

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