LOCATELLO F.

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational nonlinear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇log p(X), we extend the work of Rolland et al. (2022) that only recovers the topological...

Causal discovery methods are intrinsically constrained by the set of assumptions needed to ensure structure identifiability. Moreover additional restrictions are often imposed in order to simplify the inference task: this is the case for the Gaussian noise assumption on additive nonlinear models, which is common to many causal discovery...