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This paper introduces Observable Neural ODEs (ObsNODEs), a novel framework that combines continuous-time dynamics with causal inference to handle hidden confounding in sequential decision problems. By leveraging observability of latent states, the method enables robust forecasting under alternative treatment paths, validated across synthetic and clinical datasets. This work represents a significant advancement in applying neural ordinary differential equations to solve complex causal identification challenges in dynamic systems.
Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
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 tractable state-space formula when unobserved contemporaneous disturbances are temporally uncorrelated. This formula expresses potential-outcome distributions under alternative treatment trajectories through the measurement model, latent dynamics, and the filtering distribution over latent states. We propose Observable Neural ODEs (ObsNODEs), Neural ODE models in observable normal form that implement this tractable adjustment for causal forecasting. ObsNODEs learn continuous-time dynamics with states reconstructible from observations, enabling outcome prediction under alternative treatment paths. Experiments on synthetic, semi-synthetic, and real-world clinical data demonstrate strong performance over recent sequence models, including external validation.
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 tractable state-space formula when unobserved contemporaneous disturbances are temporally uncorrelated. This formula expresses potential-outcome distributions under alternative treatment trajectories through the measurement model, latent dynamics, and the filtering distribution over latent states. We propose Observable Neural ODEs (ObsNODEs), Neural ODE models in observable normal form that implement this tractable adjustment for causal forecasting. ObsNODEs learn continuous-time dynamics with states reconstructible from observations, enabling outcome prediction under alternative treatment paths. Experiments on synthetic, semi-synthetic, and real-world clinical data demonstrate strong performance over recent sequence models, including external validation.
Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST); Quantitative Methods (q-bio.QM)
arXiv:2604.26070 [cs.LG]
(or arXiv:2604.26070v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.26070 Focus to learn more arXiv-issued DOI via DataCite
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