Score matching enables causal discovery of nonlinear additive noise models

• URL: http://arxiv.org/abs/2203.04413v1
• Date: Tue, 8 Mar 2022 21:34:46 GMT
• Title: Score matching enables causal discovery of nonlinear additive noise models
• Authors: Paul Rolland, Volkan Cevher, Matth\"aus Kleindessner, Chris Russel, Bernhard Sch\"olkopf, Dominik Janzing and Francesco Locatello
• Abstract summary: We show how to design a new generation of scalable causal discovery methods. We propose a new efficient method for approximating the score's Jacobian, enabling to recover the causal graph.
• Score: 63.93669924730725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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