One-Step Weighting to Generalize and Transport Treatment Effect Estimates to a Target Population

Abstract

The problems of generalization and transportation of treatment effect estimates from a study sample to a target population are 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.

Keywords:

• Causal inference
• Generalization
• Observational studies
• Randomized experiments
• Transportation
• Weighting methods

Supplementary Materials

The supplementary materials include additional details on the identifying assumptions for the target average treatment effect, details on the connection between the one-step weights and inverse propensity and inverse odds weighting, details on the connection between the one-step weights and linear regression, proofs for Section 4, additional details on and results for the simulation study in Section 5, additional results for the case study in Section 6, and R code to implement the methodology.

Acknowledgments

We thank the Editor, Associate Editor, and two anonymous reviewers for helpful comments. We thank Eli Ben-Michael, Larry Han, Kosuke Imai, Yige Li, Bijan Niknam, Zhu Shen, and Yi Zhang for helpful comments and suggestions.

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work was supported through a grant from the Alfred P. Sloan Foundation (G-2020-13946) and an award from the Patient Centered Outcomes Research Initiative (PCORI, ME-2022C1-25648).