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

TL;DR

This paper presents a Bayesian machine learning algorithm, BCF-IV, for estimating heterogeneous causal effects with imperfect compliance, demonstrating superior performance and policy relevance through simulations and an educational application.

Contribution

The paper introduces BCF-IV, a novel Bayesian causal forest method that effectively handles imperfect compliance and uncovers heterogeneity in causal effects, outperforming existing techniques.

Findings

BCF-IV outperforms other methods in simulations.

It accurately detects heterogeneity in causal effects.

Application shows funding impacts student performance.

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. The results indicate that BCF-IV could be used to enhance the effectiveness of school funding on students' performance.