Rolland, Paul Thierry YvesCevher, VolkanKleindessner, MatthäusRussel, ChrisSchölkopf, BernhardJanzing, DominikLocatello, Francesco2022-06-162022-06-162022-06-162022https://infoscience.epfl.ch/handle/20.500.14299/188516This 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-theart causal discovery methods while being significantly faster.ml-aitext::conference output::conference proceedings::conference paper