@article{0874a36350d94ee5b8fb233328848095,
title = "HETEROGENEOUS CAUSAL EFFECTS WITH IMPERFECT COMPLIANCE: A BAYESIAN MACHINE LEARNING APPROACH",
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{\textquoteright} 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{\textquoteright} performances.",
author = "F.J. Bargagli-Stoffi and \{DE WITTE\}, K. and G. Gnecco",
note = "Funding Information: helpful comments. We thank the Associate Editor and two anonymous referees for Funding. Falco J. Bargagli-Stoffi acknowledges funding from the Alfred P. Sloan Foun-dation Grant for the development of “Causal Inference with Complex Treatment Regimes: Design, Identification, Estimation, and Heterogeneity” and funding from the 2021 Harvard Data Science Initiative Postdoctoral Research Fund Award. Kristof De Witte acknowledges funding from Steunpunt SONO and KU Leuven (C24/18/ 005). Funding Information: Funding. Falco J. Bargagli-Stoffi acknowledges funding from the Alfred P. Sloan Foundation Grant for the development of “Causal Inference with Complex Treatment Regimes: Design, Identification, Estimation, and Heterogeneity” and funding from the 2021 Harvard Data Science Initiative Postdoctoral Research Fund Award. Funding Information: Kristof De Witte acknowledges funding from Steunpunt SONO and KU Leuven (C24/18/ 005). Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2022.",
year = "2022",
month = sep,
doi = "10.1214/21-AOAS1579",
language = "English",
volume = "16",
pages = "1986--2009",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "3",
}