DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
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Causal effects are identifiable from a single proxy of the unobserved confounder under the SPICE completeness assumption, supported by a neural estimation framework.
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DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift
DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.
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Identifying Causal Effects Using a Single Proxy Variable
Causal effects are identifiable from a single proxy of the unobserved confounder under the SPICE completeness assumption, supported by a neural estimation framework.