Counterfactual Probabilistic Diffusion with Expert Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912584426323968 |
|---|---|
| author | Mu, Wenhao Cao, Zhi Uludag, Mehmed Rodríguez, Alexander |
| author_facet | Mu, Wenhao Cao, Zhi Uludag, Mehmed Rodríguez, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Counterfactual Probabilistic Diffusion with Expert Models Mu, Wenhao Cao, Zhi Uludag, Mehmed Rodríguez, Alexander Machine Learning Artificial Intelligence Methodology 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. |
| title | Counterfactual Probabilistic Diffusion with Expert Models |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2508.13355 |