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stat.ME2026
Testing the Validity of Instrumental Variable Sets in Causal Additive Models with Non-Constant Effects
Xichen Guo, Feng Xie, Bingbing Tang +5
Instrumental variable (IV) methods are powerful for causal effect estimation with unmeasured confounding, but in practice researchers often face a set of candidate IVs whose validi…
cs.LG2026
Learning beyond Site Bias for OOD Generalization in Brain Networks
Yingxu Wang, Kunyu Zhang, Yanwu Yang +5
Graph-based learning from functional magnetic resonance imaging (fMRI) has shown strong potential for brain network analysis. However, existing methods often degrade under cross-si…
cs.LG2026
MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
Shicheng Fan, Nour Elhendawy, Jianle Sun +4
Causal representation learning (CRL) seeks to recover latent variables with identifiability guarantees, typically up to permutation and component-wise reparameterization under appr…