Causal Diffusion Model is the first diffusion-based method to produce full probabilistic counterfactual outcome distributions for sequential interventions in longitudinal data, showing 15-30% better distributional accuracy than prior methods on a tumor-growth simulator.
Uncertainty-aware optimal treatment selection for clinical time series
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A review proposing a unified framework for intervention-aware disease trajectory modeling in clinical AI, organized around three decision tasks and three data-generating mechanisms.
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Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
Causal Diffusion Model is the first diffusion-based method to produce full probabilistic counterfactual outcome distributions for sequential interventions in longitudinal data, showing 15-30% better distributional accuracy than prior methods on a tumor-growth simulator.
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From Static Risk to Dynamic Trajectories: Toward World-Model-Inspired Clinical Prediction
A review proposing a unified framework for intervention-aware disease trajectory modeling in clinical AI, organized around three decision tasks and three data-generating mechanisms.