AI RESEARCH
Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
arXiv CS.LG
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ArXi:2501.12500v3 Announce Type: replace Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play a central role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable causal relations, which limits its applicability to climate analysis. In this paper, we.