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The paper introduces ReRoute, a framework for counterfactual 'what-if' prediction in scientific emulators that combines factual data with partial mechanistic knowledge—without needing controlled intervention data—achieving 18.2–31.8% lower climate error under CO2 distribution shifts. Its causal identification result is machine-checked in Lean, with applications to advection–diffusion systems, coupled-climate emulation, and ERA5-trained emulators.
ThousandWorlds is a benchmark dataset for machine-learning emulation of exoplanet climates, containing approximately 1800 simulations from five global climate models. Gaussian process methods outperform deep learning baselines in this low-data, multi-simulator regression task.