AI RESEARCH
Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
arXiv CS.LG
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ArXi:2604.12992v1 Announce Type: cross Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. We