论文标题
评估外部有效性对最差的亚群
Assessing External Validity Over Worst-case Subpopulations
论文作者
论文摘要
研究人群通常是从时空和时间上的有限点取样的，而边缘化组的代表性不足。为了评估随机和观察性研究的外部有效性，我们在给定规模的所有亚群中提出和评估了最坏情况的治疗效果（WTE），这可以确保积极的发现对亚群体的群体仍然有效。我们为WTE开发了半呈效率的估计器，该估计量分析了增强的反向加权估计量的外部有效性，以达到平均治疗效果。我们的交叉拟合过程利用了基于滋扰参数的灵活的非参数和机器学习的估计值，即使滋扰估计的收敛较慢，也是常规的根 - $ n $估计器。在外部有效性是核心关注的真实例子上，我们提出的框架防护卫队反对脆弱的发现，而这些发现因意外的人口变化而无效。
Study populations are typically sampled from limited points in space and time, and marginalized groups are underrepresented. To assess the external validity of randomized and observational studies, we propose and evaluate the worst-case treatment effect (WTE) across all subpopulations of a given size, which guarantees positive findings remain valid over subpopulations. We develop a semiparametrically efficient estimator for the WTE that analyzes the external validity of the augmented inverse propensity weighted estimator for the average treatment effect. Our cross-fitting procedure leverages flexible nonparametric and machine learning-based estimates of nuisance parameters and is a regular root-$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.
