Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
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 introduce the Causal Diffusion Model (CDM), the first denoising diffusion probabilistic approach explicitly designed to generate full probabilistic distributions of counterfactual outcomes under sequential interventions. CDM employs a novel residual denoising architecture with relational self-attention, capturing intricate temporal dependencies and multimodal outcome trajectories without requiring explicit ad...
| Authors | Farbod Alinezhad, Jianfei Cao, Gary J. Young et al. |
|---|---|
| Primary category | stat.ML |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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