Gradient-Based Neural DAG Learning. - Sébastien Lachapelle; Philippe Brouillard; Tristan Deleu; Simon Lacoste-Julien (ICLR, 2020)

Differentiable Causal Discovery from Interventional Data. - Philippe Brouillard; Sébastien Lachapelle; Alexandre Lacoste; Simon Lacoste-Julien; Alexandre Drouin (NeurIPS, 2020)

Typing assumptions improve identification in causal discovery. - Philippe Brouillard; Perouz Taslakian; Alexandre Lacoste; Sébastien Lachapelle; Alexandre Drouin (CLeaR, 2022)

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. - Julia Kaltenborn; Charlotte E. E. Lange; Venkatesh Ramesh; Philippe Brouillard; Yaniv Gurwicz; Chandni Nagda; Jakob Runge; Peer Nowack; David Rolnick (NeurIPS, 2023)

Towards Causal Representations of Climate Model Data. - Julien Boussard; Chandni Nagda; Julia Kaltenborn; Charlotte Emilie Elektra Lange; Philippe Brouillard; Yaniv Gurwicz; Peer Nowack; David Rolnick (CoRR, 2023)

Causal Representation Learning in Temporal Data via Single-Parent Decoding. - Philippe Brouillard; Sébastien Lachapelle; Julia Kaltenborn; Yaniv Gurwicz; Dhanya Sridhar; Alexandre Drouin; Peer Nowack; Jakob Runge; David Rolnick (CoRR, 2024)

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 (CoRR, 2024)

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