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Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models

arXiv cs.LG Medical/Healthcare AI Fangzhou Wang, Yixuan Yang, Camilla Balzarotti, Rishikesan Kamaleswaran 2026-09-18

TL;DR - This paper shows that counterfactual predictions from clinical world models depend materially on whether treatments are edited as isolated settings or coherent bundles. Bundle-aware interventions produced larger predicted patient-state changes, suggesting that single-component edits may understate treatment sensitivity.

  • An audit of 945,707 MIMIC-IV patient-hours found that many treatment components, such as dialysis parameters, only occur together.
  • Using Clin-JEPA, the authors studied 1,019 documented onsets of invasive ventilation while holding patient history and other treatments fixed.
  • Replacing a complete ventilator configuration with one from a similar real patient shifted the predicted next state more than changing any single setting, consistently across five settings.
  • The difference persisted after accounting for the magnitude of each input edit, supporting clinically coherent treatment bundles for counterfactual simulation.

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