Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

Excerpt

arXiv:2604.26070v3 Announce Type: replace Abstract: 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 conditional front-door adjustment, even in the presence of hidden confounding. We derive a general adjustment formula and show that it reduces to a tra