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stat.ML2026
Causal Learning with the Invariance Principle
Francesco Montagna, Francesco Locatello
Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) to show that assuming that the…
stat.ML2025
On the Identifiability of Causal Graphs with the Invariance Principle
Francesco Montagna
Causal discovery from i.i.d. observational data is known to be generally ill-posed. We demonstrate that if we have access to the distribution {induced} by a structural causal model…
stat.ML2024
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
Francesco Montagna, Philipp M. Faller, Patrick Bloebaum +2
Causal discovery from observational data holds great promise, but existing methods rely on strong assumptions about the underlying causal structure, often requiring full observabil…