  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.
