| Constraint-based causal structure learning with consistent separating sets H Li, V Cabeli, N Sella, H Isambert Advances in neural information processing systems 32, 2019 | 21 | 2019 |
| Reliable causal discovery based on mutual information supremum principle for finite datasets V Cabeli, H Li, M da Câmara Ribeiro-Dantas, F Simon, H Isambert Paper presented at WHY21 workshop, 35rd Conference on Neural Information …, 2021 | 3 | 2021 |
| More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study R Momal, H Li, P Trichelair, MGB Blum, F Balazard Trials 24 (1), 380, 2023 | 1 | 2023 |
| Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients MC Ribeiro-Dantas, H Li, V Cabeli, L Dupuis, F Simon, L Hettal, AS Hamy, ... arXiv preprint arXiv:2303.06423, 2023 | 1 | 2023 |
| FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings JO Terrail, Q Klopfenstein, H Li, I Mayer, N Loiseau, M Hallal, F Balazard, ... arXiv preprint arXiv:2311.16984, 2023 | | 2023 |
| Interpretable biological network reconstruction from observational data H Li Université Paris Cité, 2021 | | 2021 |
| Supporting Information Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients MC Ribeiro-Dantas, H Li, V Cabeli, L Dupuis, F Simon, L Hettal, AS Hamy, ... | | |
