Abstract: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. We derive a continuous-time adjustment formula expressing potential outcome distributions under treatment trajectories via the measurement model, latent dynamics, and the filtering distribution over latent states given observed histories. We propose Observable Neural ODEs (ObsNODEs), Neural ODE models in observable normal form for causal forecasting. ObsNODEs learn continuous-time dynamics with states reconstructible from observations, enabling outcome prediction under alternative treatment paths. Experiments on synthetic cancer data, semi-synthetic data based on MIMIC-IV, and real-world sepsis data show strong performance over recent sequence models.
| Comments: | 20 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Optimization and Control (math.OC); Statistics Theory (math.ST); Quantitative Methods (q-bio.QM) |
| MSC classes: | 34H99 (Primary) 37N25 37N35 (Secondary) |
| ACM classes: | I.2.6; I.2.8 |
| Cite as: | arXiv:2604.26070 [cs.LG] |
| (or arXiv:2604.26070v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26070 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Maik Kschischo [view email]
[v1]
Tue, 28 Apr 2026 19:18:42 UTC (483 KB)