Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach
Problem Setting
In our forthcoming paper on Annals of Applied Statistics, we propose a new method – which we call Bayesian Causal Forest with Instrumental Variable (BCF-IV) – to interpretably discover the subgroups with the largest or smallest cau... Continue reading: Heterogeneous Treatment Effects with Instrumental Variables: A Causal Machine Learning Approach
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