Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models
On the map: World models → Medical and surgical world models
Abstract (arXiv)
Counterfactual simulation with a clinical world model means fixing a patient's history, changing the treatment, and reading off the predicted response. Doing so requires deciding what counts as one intervention. In clinical settings, interventions are documented as bundles: a co-occurrence audit of 945,707 patient-hours from MIMIC-IV shows groups of components, such as every parameter of a dialysis circuit, that never appear apart, so an edit that changes one component on its own describes an hour that never occurs in the data. We hypothesize that the granularity at which an intervention is edited changes how a world model responds, and test this with Clin-JEPA, a latent world model of patient trajectories conditioned on hourly treatment text. At 1,019 documented onsets of invasive ventilation, we keep the patient's history and other treatments fixed and compare editing one ventilator setting with editing the complete configuration recorded for a real patient with the most similar recent trajectory. The complete bundle moves the predicted next state further than any single setting, consistently across all five settings, and the difference remains after accounting for how much each edit changes the model's input. Intervention granularity therefore materially affects the response of a clinical world model: single-component edits may understate treatment sensitivity, and bundle-aware editing may offer a better-supported basis for counterfactual treatment simulation.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Medical and surgical world models, one of 11 directions in World models. Builds on 11 of its 27 references that are in the map. First posted on arXiv 18 Sep 2026.
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11 of 27 references.
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- 2026ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data21 May
- 2026Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories11 May
- 2026EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories3 Feb
Other references · 8
- 2020Shortcut Learning in Deep Neural Networkscited by 1,944
- 2025V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planningcited by 769
- 2018Recurrent World Models Facilitate Policy Evolutioncited by 766
- 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 154
- 2024Zero Shot Health Trajectory Prediction Using Transformercited by 76
- 2025medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Supportcited by 8
- 2026MiraBench: Evaluating Action-Conditioned Reliability in Robotic World Modelscited by 8
- 2026Overcoming Statistical Bias in Action-Controllable World Modelscited by 6
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