2 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Optimal Transport with Heterogeneously Missing Data
Linus Bleistein, Aurélien Bellet, Julie Josse
We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely…
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) es…
Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments
Houssam Zenati, Judith Abécassis, Julie Josse +1
Uncovering causal mediation effects is of significant value to practitioners seeking to isolate the direct treatment effect from the potential mediated effect. We propose a double…
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…