One-Step weighting to generalize and transport treatment effect estimates to a target population

TL;DR

This paper introduces a one-step weighting method for better generalization and transport of treatment effect estimates to a target population, improving efficiency and robustness.

Contribution

It provides a unified one-step weighting approach with theoretical justification, connecting it to existing methods and demonstrating its advantages.

Findings

Estimator is consistent and asymptotically normal.

Method is multiply robust and semiparametrically efficient.

Simulation and case study validate improved performance.

Abstract

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.