桥锚定下目标均值在结局模型漂移下的部分识别
Bridge-Anchored Partial Identification of a Target Mean under Outcome-Model Drift
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中文总结 AI 辅助
针对结局模型漂移导致目标均值不可点识别的问题，提出利用桥结局进行部分识别，通过桥匹配方程和残差界构造识别集，并开发去偏估计量，实现稳健推断。
中文摘要 AI 辅助
将结局关系从源人群迁移到结局未观测的目标人群，要求条件结局规律在不同人群间保持稳定。当这一条件不成立（即结局模型漂移）时，目标均值无法点识别，现有的可迁移性估计量会产生偏倚。我们研究在跨队列教育和生物医学数据整合中常见的场景，其中桥结局在两个人群中均有记录，并且可以预期与目标结局一同漂移。桥将漂移分解为两部分：其可检测的部分，由桥匹配方程固定；以及一个与桥正交的残差，该残差不被任何观测变量所约束。由于目标是一个均值，残差通过一个标量敏感性参数 $\kappa$ 起作用。我们证明识别集具有一个闭式不可约核心，其宽度由残差界和桥对结局未解释的部分决定，并且其中心对残差是一阶不变的。这些结果对任何工作指数族均成立，点识别仅作为 $\kappa=0$ 的基准出现。我们开发了一个去偏的、漂移增强的估计量，该估计量在锚定敏感性下是半参数有效的，对桥识别漂移具有双重稳健性，并为该集合产生速率稳健的 Imbens-Manski 推断。与桥盲敏感性分析（必须约束整个漂移通道）不同，所提出的分析仅约束其桥正交部分。模拟证实，漂移估计量在共同漂移下是无偏的，核心宽度遵循其闭式形式，并且一旦超过残差界，集合明显失效。动机场景是两个调查队列的整合，其中幼儿园数学结局仅在一个队列中可用。
英文摘要
Transporting an outcome relationship from a source population to a target population where the outcome is unobserved requires the conditional outcome law to be stable across populations. When it is not (outcome-model drift), the target mean is not point-identified and existing transportability estimators are biased. We study settings, common in cross-cohort educational and biomedical data integration, in which bridge outcomes are recorded in both populations and can be expected to drift alongside the target outcome. The bridge splits the drift into a component it can detect, fixed by a bridge-matching equation, and a residual orthogonal to the bridge that no observed quantity restricts. Because the target is a mean, the residual acts through a single scalar sensitivity parameter $κ$. We prove that the identified set has a closed-form irreducible core, whose width is governed by the residual bound and by how much of the outcome the bridge leaves unexplained, and that its center is first-order invariant to the residual. These results hold for any working exponential family, with point identification arising only as the $κ=0$ benchmark. We develop a debiased, drift-augmented estimator that is semiparametrically efficient at anchored sensitivity, doubly robust conditional on the bridge-identified drift, and yields rate-robust Imbens-Manski inference for the set. Unlike a bridge-blind sensitivity analysis, which must bound the entire drift channel, the proposed analysis bounds only its bridge-orthogonal part. Simulations confirm that the drift estimator is unbiased under co-drift, that the core width follows its closed form, and that the set fails visibly once the residual bound is exceeded. The motivating setting is the integration of two survey cohorts whose kindergarten mathematics outcome is available in only one.
