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08/07/2023
Variable importance for causal forests: breaking down the heterogeneity of treatment effects
Causal random forests provide efficient estimates of heterogeneous treat...
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01/31/2023
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022...
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05/25/2021
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications invo...
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02/26/2021
MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA
Variable importance measures are the main tools to analyze the black-box...
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04/29/2020
Interpretable Random Forests via Rule Extraction
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a...
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08/19/2019