Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity

Excerpt

arXiv:2510.19138v2 Announce Type: replace-cross Abstract: Granger causality characterizes directed predictive dependencies in multivariate time series, but recovering such dependencies becomes challenging in the presence of latent confounding. Cross-environment invariance provides a natural source of information in heterogeneous settings, yet invariance alone can be insufficient: when latent-to-observed mechanisms remain stable, hidden confounders can induce predictive dependencies that are just