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Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
Causal inference in continuous-time sequential decision problems is challenged by hidden confounding and partially observed states. We show that, under explicit structural assumptions, observability of the latent state enables identification of dynamic treatment effects through a continuous-time con
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