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