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This paper demonstrates how to recover causal graphs from the score of thedata distribution in non-linear additive (Gaussian) noise models. Using scorematching algorithms as a building block, we show how to design a new generationof scalable causal discovery methods. To showcase our approach, we also proposea new efficient method for approximating the score's Jacobian, enabling torecover the causal graph. Empirically, we find that the new algorithm, calledSCORE, is competitive with state-of-the-art causal discovery methods whilebeing significantly faster.

Score matching enables causal discovery of nonlinear additive noise models