Philippe Brouillard

Publications

Learning a Spatial Partitioning and its Causal Relations from Temporal Data. Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick. At Causal Learning and Reasoning (CLeaR), 2026.

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Dhanya Sridhar, Alexandre Drouin. At Causal Learning and Reasoning (CLeaR), 2025.

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The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad P. Kording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar. At Workshop at the International Conference of Learning Representation (ICLR), 2025.

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Typing assumptions improve identification in causal discovery - theory and algorithms. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Causal Learning and Reasoning (CLeaR), 2022.

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Typing assumptions improve identification in causal discovery - Report and comments on future directions. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2021.

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Typing assumptions improve identification in causal discovery. Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin. At Workshop at the International Conference on Machine Learning (ICML), 2021.

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Differentiable Causal Discovery from Interventional Data. Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lcoste-Julien, Alexandre Drouin. At Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Gradient-Based Neural DAG Learning with Interventions. Philippe Brouillard, Alexandre Drouin, Sébastien Lachapelle, Alexandre Lacoste, Simon Lcoste-Julien. At Workshop at the International Conference on Learning Representations (ICLR), 2020.

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