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This paper extends causal forests, a tree-ensemble method for estimating heterogeneous treatment effects, with a Hoeffding functional decomposition so that predictions split into per-covariate and pairwise-interaction contributions. The main claim is a consistency result: the estimator converges to the decomposition of the true CATE function under standard causal-forest assumptions. For ML readers, it is a concrete example of making tree ensembles interpretable without giving up their flexibility, though the evidence so far is simulated and limited real-data experiments.
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
arXiv:2610.16447v1 Announce Type: cross 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 identification of covariates driving heterogeneity remains a challenging and open problem. To address this limitation, we introduce CausalTreeHFD, an interpretable extension of causal forests, based on the Hoeffding functional decomposition adapted to tree ensembles. Our approach decomposes predictions of conditional average treatment effects (CATE) into contributions from individual covariates and pairwise interactions, providing a direct characterization of the factors driving treatment effect heterogeneity. We establish the consistency of CausalTreeHFD, which converges towards the Hoeffding decomposition of the underlying CATE function, under standard assumptions for the analysis of causal forests. Experiments on simulated and real datasets show that CausalTreeHFD accurately identifies relevant covariates and interactions, and precisely recovers their contributions to heterogeneous treatment effects.
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 identification of covariates driving heterogeneity remains a challenging and open problem. To address this limitation, we introduce CausalTreeHFD, an interpretable extension of causal forests, based on the Hoeffding functional decomposition adapted to tree ensembles. Our approach decomposes predictions of conditional average treatment effects (CATE) into contributions from individual covariates and pairwise interactions, providing a direct characterization of the factors driving treatment effect heterogeneity. We establish the consistency of CausalTreeHFD, which converges towards the Hoeffding decomposition of the underlying CATE function, under standard assumptions for the analysis of causal forests. Experiments on simulated and real datasets show that CausalTreeHFD accurately identifies relevant covariates and interactions, and precisely recovers their contributions to heterogeneous treatment effects.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.16447 [stat.ML]
(or arXiv:2610.16447v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.16447 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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