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stat.ML2026
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
Gaëtan Patinier, Clément Bénard, Margaux Törnqvist
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…
stat.ML2023
MMD-based Variable Importance for Distributional Random Forest
Clément Bénard, Jeffrey Näf, Julie Josse
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In th…
stat.ML2023★ 2 cited
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Clément Bénard, Julie Josse
Causal random forests provide efficient estimates of heterogeneous treatment effects. However, forest algorithms are also well-known for their black-box nature, and therefore, do n…