Information Set Emulation: Causal Certificates for AI Derived EHR Features
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信息集仿真：AI 提取电子健康记录特征的因果证书
- 研究问题
- AI 从电子健康记录中重建的特征，在什么证据条件下支持确定的因果效应主张，又何时只能报告多个兼容结论？
- 方法与思路
- 作者为特征附加来源、临床与记录时间、决策时可用性及因果角色证据，在锁定目标试验后用兼容因果世界刻画信息歧义。
- 材料陈述的贡献
- 摘要将经典极小极大半径恒等式与健康记录观测映射、可审计证书架构结合，并给出识别及交叉拟合增广逆概率加权估计。
- 阅读关注点
- 核对交换性、正值性和辅助估计一致性，以及兼容像非空紧致条件；区分精确的信息半径与角色分析结果的离散程度。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核验；全部实验均为合成实验，不能据此判断真实临床记录中的审计与估计表现。
原文摘要
AI and large language models can recover clinically meaningful features from electronic health records (EHRs), but predictive usefulness does not establish admissibility for causal inference. We introduce information set emulation: an AI typed lift attaches source evidence, clinical and recording times, decision-time availability, representation version, proposed causal roles, and unresolved ambiguity to extracted features under a locked target trial. Causal certificates record auditable evidence for those roles. Features with unresolved downstream roles are routed to compatible reporting or separate analyses. Typed evidence defines an observational fiber of causal worlds consistent with the observed law. The locked scalar estimand maps this fiber to a compatible image whose squared Chebyshev radius equals the residual minimax mean squared error when the image is nonempty and compact. This classical identity provides a target-specific measure of information ambiguity. The contribution is its integration with a joint EHR observation map and an auditable certificate architecture. Under explicit exchangeability, positivity, and nuisance-consistency conditions, we give identification and cross-fitted augmented inverse probability weighted estimation, distinguishing empirical and population targets. An EHR compression-drift identity separates the roles of frame presence, treatment assignment, and outcome observation. Artificial simulations and a common-law finite-world example illustrate estimation failures and information-radius reduction. Synthetic Phase 0 notes demonstrate audit diagnostics; a separate role-specific analysis spread illustrates routing and is not an exact fiber radius. All experiments are synthetic. The framework specifies when reconstructed information can support a point claim and when compatible reporting is required.