{"ID":2881303,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13355","arxiv_id":"2508.13355","title":"Counterfactual Probabilistic Diffusion with Expert Models","abstract":"Predicting counterfactual distributions in complex dynamical systems is essential for scientific modeling and decision-making in domains such as public health and medicine. However, existing methods often rely on point estimates or purely data-driven models, which tend to falter under data scarcity. We propose a time series diffusion-based framework that incorporates guidance from imperfect expert models by extracting high-level signals to serve as structured priors for generative modeling. Our method, ODE-Diff, bridges mechanistic and data-driven approaches, enabling more reliable and interpretable causal inference. We evaluate ODE-Diff across semi-synthetic COVID-19 simulations, synthetic pharmacological dynamics, and real-world case studies, demonstrating that it consistently outperforms strong baselines in both point prediction and distributional accuracy.","short_abstract":"Predicting counterfactual distributions in complex dynamical systems is essential for scientific modeling and decision-making in domains such as public health and medicine. However, existing methods often rely on point estimates or purely data-driven models, which tend to falter under data scarcity. We propose a time s...","url_abs":"https://arxiv.org/abs/2508.13355","url_pdf":"https://arxiv.org/pdf/2508.13355v2","authors":"[\"Wenhao Mu\",\"Zhi Cao\",\"Mehmed Uludag\",\"Alexander Rodríguez\"]","published":"2025-08-18T20:44:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ME\"]","methods":"[\"Diffusion Model\"]","has_code":false}