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Differentiable Causal Discovery from Interventional Data
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin

Wed Dec 09 08:10 AM -- 08:20 AM (PST) @ Orals & Spotlights: Probabilistic/Causality
in Orals & Spotlights Track 19: Probabilistic/Causality »

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.

[ Paper ]

Author Information

Philippe Brouillard (Mila, Université de Montréal)
Sébastien Lachapelle (Mila)
Alexandre Lacoste (Element AI)
Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal)

Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.

Alexandre Drouin (Element AI)

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