The problem of generalization and transportation of treatment effect estimates from a study sample to a target population is central to empirical research and statistical methodology. In both randomized experiments and observational studies, weighting methods are often used with this objective. Traditional methods construct the weights by separately modeling the treatment assignment and study selection probabilities and then multiplying functions (e.g., inverses) of their estimates. In this work, we provide a justification and an implementation for weighting in a single step. We show a formal connection between this one-step method and inverse probability and inverse odds weighting. We demonstrate that the resulting estimator for the target average treatment effect is consistent, asymptotically Normal, multiply robust, and semiparametrically efficient. We evaluate the performance of the one-step estimator in a simulation study. We illustrate its use in a case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California. We provide R code implementing the methodology.

翻译：治疗效应估计从研究样本推广和迁移到目标人群的问题是实证研究和统计方法的核心。在随机实验和观察性研究中，加权方法常被用于实现这一目标。传统方法通过分别建模治疗分配和研究选择概率，然后乘以这些概率函数（如倒数）来构建权重。本文提出了一步加权的理论基础与实现方法。我们证明了一步法与逆概率加权和逆比值加权之间的形式化联系，并表明由此得到的目标人群平均治疗效应估计量具有一致性、渐近正态性、多重稳健性和半参数有效性。通过模拟研究评估了一步估计量的性能，并以加利福尼亚州黑人男性中医生种族多样性对预防性医疗保健利用的影响为例进行实证分析。本文还提供了实现该方法的R代码。