Counterfactual Probabilistic Diffusion with Expert Models

• URL: http://arxiv.org/abs/2508.13355v2
• Date: Thu, 11 Sep 2025 20:38:41 GMT
• Title: Counterfactual Probabilistic Diffusion with Expert Models
• Authors: Wenhao Mu, Zhi Cao, Mehmed Uludag, Alexander Rodríguez,
• Abstract summary: We propose a time series diffusion-based framework that incorporates guidance from imperfect expert models.<n>Our method, ODE-Diff, bridges mechanistic and data-driven approaches, enabling more reliable and interpretable causal inference.
• Score: 44.96279296893773
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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