评估最差情况亚群的外部有效性
Assessing External Validity Over Worst-case Subpopulations
摘要：
研究人群通常是从有限的空间和时间点抽样，边缘化群体的代表性不足。为了评估随机和观察性研究的外部有效性，我们提出并评价了给定规模的所有亚群的最差处理效应（WTE），这保证了阳性结果在亚群中仍然有效。我们开发了一个半参数有效估计的WTE分析的外部有效性的增广逆倾向加权估计的平均处理效应。我们的交叉拟合过程利用滋扰参数的灵活的非参数和基于机器学习的估计，并且即使当滋扰估计收敛得更慢时，也是常规的根$n$估计器。在外部有效性是核心问题的真实例子中，我们提出的框架可以防止因意外的人口变化而无效的脆弱发现。
英文摘要：
Study populations are typically sampled from limited points in space and time, and marginalized groups are underrepresented. To assess the external validity of randomized and observational studies, we propose and evaluate the worst-case treatment effect (WTE) across all subpopulations of a given size, which guarantees positive findings remain valid over subpopulations. We develop a semiparametrically efficient estimator for the WTE that analyzes the external validity of the augmented inverse propensity weighted estimator for the average treatment effect. Our cross-fitting procedure leverages flexible nonparametric and machine learning-based estimates of nuisance parameters and is a regular root-$n$ estimator even when nuisance estimates converge more slowly. On real examples where external validity is of core concern, our proposed framework guards against brittle findings that are invalidated by unanticipated population shifts.
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