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

29 May 2019Falco J. Bargagli-StoffiKristof De-WitteGiorgio Gnecco

This paper introduces an innovative Bayesian machine learning algorithm to draw 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) algorithm outperforms other machine learning techniques tailored for causal inference (namely, Generalized Random Forest and Causal Trees with Instrumental Variable) in estimating the causal effects... (read more)

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