Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
This work introduces Observable Neural ODEs (ObsNODEs) to identify treatment effects in continuous-time state-space models with hidden confounders. The core finding is that observing the latent state is necessary for identifying dynamic treatment effects, leading to a new continuous-time causal adjustment formula.