Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
2026-04-14 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method called the Causal Diffusion Model (CDM) to better predict what would happen if different treatments were given over time, especially when patient conditions keep changing. Unlike previous methods, CDM can estimate a full range of possible outcomes and handle tricky, time-based factors without needing complicated adjustments. Tests on simulated tumor growth show CDM predicts these outcomes more accurately and reliably than existing approaches. This makes CDM a useful tool for making decisions in medicine and other areas where treatment effects unfold over time.
counterfactual outcomeslongitudinal datatime-dependent confoundingdenoising diffusion probabilistic modelsself-attentionpharmacokinetic-pharmacodynamic modelinginverse-probability weightingdistributional accuracyRMSEcausal inference
Authors
Farbod Alinezhad, Jianfei Cao, Gary J. Young, Brady Post
Abstract
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 adjustments (e.g., inverse-probability weighting or adversarial balancing) for confounding. In rigorous evaluation on a pharmacokinetic-pharmacodynamic tumor-growth simulator widely adopted in prior work, CDM consistently outperforms state-of-the-art longitudinal causal inference methods, achieving a 15-30% relative improvement in distributional accuracy (1-Wasserstein distance) while maintaining competitive or superior point-estimate accuracy (RMSE) under high-confounding regimes. By unifying uncertainty quantification and robust counterfactual prediction in complex, sequentially confounded settings, without tailored deconfounding, CDM offers a flexible, high-impact tool for decision support in medicine, policy evaluation, and other longitudinal domains.