Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

ArXi:2604.26070v1 Announce Type: new Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with time-varying interventions, observability of the latent dynamics from observed data is necessary for identifying dynamic treatment effects, linking control-theoretic observability to causal identifiability, even when hidden confounders affect both treatments and outcomes.