Same Predictions, Different Harms: Causal Auditing of Patient World Models

arXiv:2610.05198 · stat.ME, cs.AI · Submitted 2026-10-04 · Read on arXiv

stat.ME, cs.AI

Submitted: 2026-10-04

Updated: 2026-10-04

Comments: 35 pages, 4 figures. Includes appendices

Code: https://github.com/clinicalml/gumbel-max-scm

License: http://creativecommons.org/licenses/by/4.0/

The gist: Patient world models used for clinical trial simulation can agree on transition kernels and arm-specific risks, yet disagree on the fraction of patients harmed by switching treatment---the

Terminology

Abstract

Patient world models used for clinical trial simulation can agree on transition kernels and arm-specific risks, yet disagree on the fraction of patients harmed by switching treatment---the counterfactual quantity that matters for intervention-aware reasoning. We audit this reliability gap in a two-stage shared-response SCM: a categorical intermediate health state is followed by common terminal care. Under independent stages, the sharp harm interval has closed-form endpoints for at most three intermediate states, with an exactness boundary at four states. Declared dependence and response-mismatch budgets yield calibrated outer bounds when stage independence or complete mediation is relaxed; in a symmetric three-state model the entire sensitivity frontier is sharp, [0, 1/2,1/3+(ρ+δ)/2], and shows exactly how budgets erase the gain over endpoint-only bounds. Two eight-variable response LPs propagate interventional uncertainty for finite-sample audits. Exact witnesses verify attainability. On public clinical simulators (EpiCare; sepsis), native configurations show little resolved stage dependence and no additional joint-compatibility gain over pairwise transport---honest negative results for reliability claims. All experiments are locally reproducible; guarantees remain conditional on the stated causal model. The results provide a concrete protocol for deciding when a patient world model is safe to trust for counterfactual harm.

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