2 citations · 2 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Clément Bénard, Brian Staber, Sébastien Da Veiga
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize…