continuous-time models

Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

This research addresses challenges in continuous-time causal inference due to hidden confounders, demonstrating that observability of latent dynamics is crucial for identifying dynamic treatment effects. It proposes Observable Neural ODEs (ObsNODEs), a novel model for causal forecasting by learning reconstructible continuous-time dynamics.