Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
arXiv:2610.16447v1 Announce Type: new Abstract: 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 identific…
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- 2026-10-14 04:00 · arXiv stat.ML
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition