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Oral
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal discovery methods. To showcase our approach, we also propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph. Empirically, we find that the new algorithm, called SCORE, is competitive with state-of-the-art causal discovery methods while being significantly faster.
Author Information
Paul Rolland (Ecole Polytechnique Fédérale de Lausanne)
Volkan Cevher (EPFL)
Matthäus Kleindessner (Amazon)
Chris Russell (Amazon)
Dominik Janzing (Amazon Research Tübingen)
Bernhard Schölkopf (Amazon / MPI Intelligent Systems)
Francesco Locatello (Amazon Lablet)
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2022 Poster: Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models »
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