Propensity-score based methods for causal inference in observational studies with non-binary treatments
File(s)
Accepted version
Author(s)
Zhao, Shandong
van Dyk, David
Imai, Kosukie
Type
Journal Article
Abstract
Propensity score methods are a part of the standard toolkit for applied researchers who wish to ascertain causaleffects from observational data. While they were originally developed for binary treatments, several researchershave proposed generalizations of the propensity score methodology for non-binary treatment regimes. Suchextensions have widened the applicability of propensity score methods and are indeed becoming increasinglypopular themselves. In this article, we closely examine two methods that generalize propensity scores in thisdirection, namely, the propensity function (pf), and the generalized propensity score (gps), along with twoextensions of thegpsthat aim to improve its robustness. We compare the assumptions, theoretical properties,and empirical performance of these methods. On a theoretical level, thegpsand its extensions are advantageousin that they are designed to estimate the full dose response function rather than the average treatment effectthat is estimated with thepf. We comparegpswith a newpfmethod, both of which estimate the doseresponse function. We illustrate our findings and proposals through simulation studies, including one based onan empirical study about the effect of smoking on healthcare costs. While our proposedpf-based estimatorpreforms well, we generally advise caution in that all available methods can be biased by model misspecificationand extrapolation.
Date Issued
2020-03-01
Date Acceptance
2019-10-23
Citation
Statistical Methods in Medical Research, 2020, 29 (3), pp.709-727
ISSN
0962-2802
Publisher
SAGE Publications
Start Page
709
End Page
727
Journal / Book Title
Statistical Methods in Medical Research
Volume
29
Issue
3
Copyright Statement
© The Author(s) 2019. Published by Sage Publications.
Subjects
0104 Statistics
1117 Public Health and Health Services
Statistics & Probability
Publication Status
Published
Date Publish Online
2020-03-18