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This paper introduces CLAM, a method for estimating localized causal effects from coarse-resolution data by jointly learning causal mechanisms and a disaggregation mapping, with applications in public health and environmental policy.
This paper introduces EddyFlow, a deep learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization. It achieves strong zero-shot performance and near-ideal spectral fidelity across multiple ocean regions.