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arXiv · 2203.04413

Score matching enables causal discovery of nonlinear additive noise models

Paul Rolland·Volkan Cevher·Matthäus Kleindessner·Chris Russel·Bernhard Schölkopf·Dominik Janzing·Francesco Locatello
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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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Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello. 2022-03-08. Score matching enables causal discovery of nonlinear additive noise models. https://arxiv.org/abs/2203.04413

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