Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach

This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal e ects 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 e ects 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 e ects in instrumental variable scenarios and, in turn, provides the policy-makers with a relevant tool for targeted policies. Its empirical application evaluates the e ects of additional funding on students' performances. The results indicate that BCF-IV could be used to enhance the e ectiveness of school funding on students' performance.

Jaar van publicatie:2022

Toegankelijkheid:Open