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arXiv · 2508.13355

Counterfactual Probabilistic Diffusion with Expert Models

Wenhao Mu·Zhi Cao·Mehmed Uludag·Alexander Rodríguez
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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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Wenhao Mu, Zhi Cao, Mehmed Uludag, Alexander Rodríguez. 2025-09-11. Counterfactual Probabilistic Diffusion with Expert Models. https://arxiv.org/abs/2508.13355

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