Spectral Causal Identifiability Under Partial and Noisy Observation

Causal identification typically requires rich interventions or full-state observability. We show that partial, noisy observations can still identify causal structure when signals admit a stable spectral decomposition with separation conditions. We derive identifiability criteria in terms of mode observability, spectral gap structure, and noise geometry, and provide constructive recovery guarantees.