Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach
This provides policy-makers with a tool for targeted policies in instrumental variable scenarios, but it is incremental as it builds on existing causal inference techniques.
The paper tackles the problem of estimating heterogeneous causal effects with imperfect compliance by introducing a Bayesian machine learning algorithm, BCF-IV, which outperforms other methods in simulations by controlling error rates at leaves' level and is applied to show potential for enhancing school funding effectiveness on student performance.
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.