When Are Neural Interaction Discoveries Real? Identifiability, Recoverability, and a Pre-Fit Diagnostic
Researchers have developed a new diagnostic tool to determine if interactions identified by neural time-series models are genuine or artifacts of model flexibility. The method focuses on the geometry of the input data's support rather than the specific neural architecture used. A pre-fit diagnostic, based on the effective rank of the joint lag-block covariance, can predict the feasibility of recovering interaction terms before model fitting. AI
IMPACT Provides a method to validate findings from neural time-series models, ensuring discovered interactions are data-driven and not model artifacts.