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：研究样本中处理效应估计的推广与迁移问题，是实证研究和统计方法学的核心议题。在随机实验和观察性研究中，加权方法常被用于实现此目标。传统方法通过分别建模处理分配概率和研究选择概率，再对估计值进行函数变换（如取倒数）来构建权重。本研究提出一种单步加权方法的理论依据与实现方案。我们揭示了该一步式方法与逆概率加权法及逆比值加权法之间的形式化联系，证明所得目标人群平均处理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该估计量的性能，并以加利福尼亚州非裔男性群体为例，阐释该方法在医生种族多样性对预防性医疗利用影响研究中的应用。我们提供了实现该方法的R代码。