Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition

The estimation of heterogeneous treatment effects is a central objective of causal inference. However, state-of-the-art algorithms to solve this task are based on machine learning principles, and thus behave as black boxes. Consequently, the identification of covariates driving heterogeneity remains