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Shortcuts for causal discovery of nonlinear models by score matching
The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data.
Scalable Causal Discovery with Score Matching
This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models.
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability.
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