Propensity score-based methods for causal inference in observational studies with non-binary treatments. (March 2020)

• Record Type: Journal Article
• Authors: Zhao, Shandong; van Dyk, David A; Imai, Kosuke
• Other Names: Austin Peter guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 29:Number 3(2020)
• Page Start: 709
• Page End: 727
• Publication Date: 2020-03
• Subjects: Covariate adjustment -- generalized propensity score -- model diagnostics -- propensity function -- smooth coefficient model -- stabilized weights -- nonparametric models; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280219888745
• Languages: English
• ISSNs: 0962-2802

Abstract

Propensity score methods are a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. Such extensions have widened the applicability of propensity score methods and are indeed becoming increasingly popular themselves. In this article, we closely examine two methods that generalize propensity scores in this direction, namely, the propensity function (PF), and the generalized propensity score (GPS), along with two extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. On a theoretical level, the GPS and its extensions are advantageous in that they are designed to estimate the full dose response function rather than the average treatment effect that is estimated with the PF. We compare GPS with a new PF method, both of which estimate the dose response function. We illustrate our findings and proposals through simulation studies, including one based on an empirical study about the effect of smoking on healthcare costs. While our proposed PF-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation.