Related papers: Score matching through the roof: linear, nonlinear, and latent variables causal discovery

Score matching through the roof: linear, nonlinear, and latent variables causal discovery

URL: http://arxiv.org/abs/2407.18755v1
Date: Fri, 26 Jul 2024 14:09:06 GMT
Title: Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Authors: Francesco Montagna, Philipp M. Faller, Patrick Bloebaum, Elke Kirschbaum, Francesco Locatello,
Abstract summary: Causal discovery from observational data holds great promise. Existing methods rely on strong assumptions about the underlying causal structure. We propose a flexible algorithm for causal discovery across linear, nonlinear, and latent variable models.
Score: 18.46845413928147
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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