East Coast Ballroom
Propensity score stratification: new insights to an old problem. (307868)
*Roland Albert Matsouaka, Duke UniversityKeywords: propensity score stratification, Mantel-Haenzsel weights, Inverse-variance weights, confounding, wild bootstrap
Propensity score methods (PSMs) have gained immense popularity in the last three decades. They are commonly used to control for confounding and reduce bias in the assessment of causal treatment effects. Of the four major PSMs used in the literature, the propensity score stratification (PSS) method, despite its many practical advantages, often yields results that are less reliable and for which there are limited explanations as to why.
For this presentation, we take a closer look at the different weights used to aggregate stratum-specific treatment effects into the overall average treatment effect (ATE). We demonstrate that these weights rely on a set of underlying assumptions that are not always true and rarely verified in practice, which explains in part some the discrepancies in the results encountered in data analyses. As an alternative, we introduce different efficient weight estimates for the ATE that are congruent to the PSS framework. We assess the performances of the proposed weight estimates through simulation studies, considering several data-generating scenarios. Finally, we illustrate the proposed method using the Linder and NC birthweight datasets.