Same Predictions, Different Harms: Causal Auditing of Patient World Models
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.
Sources
- Robust Counterfactual Inference in Markov Decision Processes
- Optimal Correlation for Bernoulli Trials with Covariates
- Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
- General Probabilities of Causation with Causal Knowledge
- Bounding Probabilities of Causation with Partial Causal Diagrams
Related papers
- Doubly robust inference via calibration
- Bayesian Empirical Bayes: Simultaneous Inference from Probabilistic Symmetries
- Optimal Watermark Localization in Mixed-Source Large Language Model Texts
- Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
- A Greedy PDE Router for Blending Neural Operators and Classical Methods
- Policy Learning with Weak Signals