Score matching enables causal discovery of nonlinear additive noise models
This addresses causal discovery for researchers and practitioners in fields like machine learning and statistics, representing a novel method for a known bottleneck.
The paper tackles the problem of recovering causal graphs from data in nonlinear additive noise models by using score matching algorithms, resulting in a new algorithm called SCORE that is competitive with state-of-the-art methods and significantly faster.
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.