发表机构
University of California, Berkeley; Georgia Institute of Technology（加州大学伯克利分校; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文区分流行病学世界模型中尊重设计与忽略设计的模型，通过大规模试验证明仅事实重建不足，需检验对比是否保持设计隐含的不变性。
AI 中文摘要
流行病学的世界模型从受研究设计影响的记录中学习，这些设计包括分配、抽样、测量及相关过程。因此，模型在重建观察到的轨迹时，可能学习到依赖于记录收集方式的干预对比。我们将研究设计形式化为对世界模型潜在世界接口、作用机制、观测似然和目标读出的约束。这区分了尊重设计的模型（编码这些约束）与忽略设计的模型（拟合选定的记录，而不将分配和观测与预期的干预问题联系起来）。利用一项大规模整群随机检验阴性试验，我们保持潜在结构和拟合设置固定，比较了忽略设计的病例计数模型与尊重设计的检验阴性观测模型。两个模型都实现了相当的事实重建。然而，在500次配对重采样实验中，不同相对采样强度下，忽略设计模型的中位对比变化显著，而尊重设计模型仅边际变化。当采样在群组间也变化时，同样的定性分离成立。因此，仅凭事实重建并不能确立设计对齐；测试拟合对比是否保持设计隐含的观测过程不变性提供了更锐利的评估。
英文摘要
World models for epidemiology learn from records shaped by study designs, including assignment, sampling, measurement, and related processes. A model may therefore reconstruct observed trajectories while learning an intervention contrast that depends on how records were collected. We formalize study design as constraints on a world model latent-world interface, action mechanism, observation likelihood, and target readout. This distinguishes design-respecting models, which encode these constraints, from design-ignoring models, which fit selected records without relating assignment and observation to the intended intervention question. Using a large scale cluster-randomized test-negative trial, we hold the latent structure and fitting settings fixed and compare a design-ignoring case-count model with a design-respecting test-negative observation model. Both models achieve comparable factual reconstruction. Yet across 500 paired resampling experiments at different relative sampling intensity, median contrast changes are substantial for the design-ignoring model, versus merely marginal for the design-respecting model. The same qualitative separation holds when sampling also varies across clusters. Thus, factual reconstruction alone does not establish design alignment; testing whether fitted contrasts preserve design-implied observation-process invariances provides a sharper evaluation.
