What is a good matching of probability measures? A counterfactual lens on transport maps. L. De Lara, L. Ganassali. (2025). Preprint

A clarification on the links between potential outcomes and do-interventions. L de Lara (2025). Journal of Causal Inference

Counterfactual Robustness: a framework to analyze the robustness of Causal Generative Models across interventions. M. Benhamza, M. Clausel, M. Tami. (2025). ECML

Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering. C. Yvernes, E. Devijver, A. Ribeiro, M. Clausel, E. Gaussier (2025). NeurIPS

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
C. Yvernes, E. Devijver, E. Gaussier (2025). UAI

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
S. Dong, M. Sebag, K. Uemura, A. Fujii, S. Chang, Y. Koyanagi, K. Maruhashi (2025) AAAI

Identifiability of total effects from abstractions of time series causal graphs,
C.K. Assaad, E. Devijver, E. Gaussier, G. Goessler, A. Meynaoui (2024). UAI

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms,
D. Bystrova, C.K. Assaad, J. Arbel, E. Devijver, E. Gaussier, W. Thuillier (2024). TMLR

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems,
L. Zan, A. Ait-Bachir, C. K. Assaad, E. Devijver, E. Gaussier (2024). CIKM

Learning Large Causal Structure from Inverse Covariance Matrix via Matrix Decomposition. S. Dong, K. Uemura, A. Fujii, S. Chang, Y. Koyanagi, K. Maruhashi, and M. Sebag (2024). Preprint

Learning structural causal models through deep generative models: methods, guarantees, and challenges. A. Poinsot, A. Leite, N. Chesneau, M. Sebag, M. Schoenauer. (2024). IJCAI

Difference graph over two populations: Implicit Difference Inference algorithm,
D. Bystrova, E. Devijver, V. Manucharian, J. Mondet, P. Mossuz (2024), 9th Causal Inference Workshop at UAI 2024