Détail du document
Identifiant
oai:arXiv.org:2407.18755
Sujet
Statistics - Machine Learning Computer Science - Artificial Inte... Statistics - MethodologyAuteur
Montagna, Francesco Faller, Philipp M. Bloebaum, Patrick Kirschbaum, Elke Locatello, FrancescoCatégorie
Computer Science
Année
2024
Date de référencement
31/07/2024
Mots clés
latent variables scoreRésumé
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observability of all relevant variables.
We tackle these challenges by leveraging the score function $\nabla \log p(X)$ of observed variables for causal discovery and propose the following contributions.
First, we generalize the existing results of identifiability with the score to additive noise models with minimal requirements on the causal mechanisms.
Second, we establish conditions for inferring causal relations from the score even in the presence of hidden variables; this result is two-faced: we demonstrate the score's potential as an alternative to conditional independence tests to infer the equivalence class of causal graphs with hidden variables, and we provide the necessary conditions for identifying direct causes in latent variable models.
Building on these insights, we propose a flexible algorithm for causal discovery across linear, nonlinear, and latent variable models, which we empirically validate.
Montagna, Francesco,Faller, Philipp M.,Bloebaum, Patrick,Kirschbaum, Elke,Locatello, Francesco, 2024, Score matching through the roof: linear, nonlinear, and latent variables causal discovery
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