Transport-based Counterfactual Models. - Lucas de Lara; Alberto González-Sanz; Nicholas Asher; Jean-Michel Loubes (CoRR, 2021)

GAN Estimation of Lipschitz Optimal Transport Maps. - Alberto González-Sanz; Lucas de Lara; Louis Béthune; Jean-Michel Loubes (CoRR, 2022)

Counterfactual Models for Fair and Adequate Explanations. - Nicholas Asher; Lucas de Lara; Soumya Paul; Chris Russell 001 (Mach. Learn. Knowl. Extr., 2022)

Modèles contrefactuels pour un apprentissage machine explicable et juste : une approche par transport de masse. (Counterfactual models for hair and explainable machine learning : a mass transportation approach). - Lucas de Lara (2023)

Diffeomorphic Registration Using Sinkhorn Divergences. - Lucas de Lara; Alberto González-Sanz; Jean-Michel Loubes (SIAM J. Imaging Sci., 2023)

On the nonconvexity of some push-forward constraints and its consequences in machine learning. - Lucas de Lara; Mathis Deronzier; Alberto González-Sanz; Virgile Foy (CoRR, 2024)

Transport-based Counterfactual Models. - Lucas de Lara; Alberto González-Sanz; Nicholas Asher; Laurent Risser; Jean-Michel Loubes (J. Mach. Learn. Res., 2024)

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning. - Lucas de Lara (CoRR, 2025)