Score matching enables causal discovery of nonlinear additive noise models

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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.

Paul Rolland, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, Francesco Locatello · 2022

Related benchmarks

Task	Dataset	Result
Causal Discovery	Sachs real-world data protein signaling network	SHD12	41
Causal Structure Learning	Sachs	SHD12	38
Causal Discovery	Syntren	F1 Score18.3	22
Causal Discovery	SF5 (n=30, h=5)	FDR55	18
Causal Discovery	ER5 (n=30, h=5)	FDR0.66	18
Causal Discovery	Synthetic ER3 n=50, h=3 (test)	FDR69	17
Causal Discovery	Synthetic SF3 n=50, h=3 (test)	FDR64	17
Causal Discovery	Causal Discovery Suite 30% MAR missingness	Coverage15	16
Causal Discovery	15 Real-world Causal Discovery Benchmark Datasets	Coverage15	16
Causal Discovery	FoundCause 10% MAR missingness	AUROC0.521	16