Open Access
August 2017 Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas
Michael J. Lopez, Roee Gutman
Statist. Sci. 32(3): 432-454 (August 2017). DOI: 10.1214/17-STS612


The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments.


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Michael J. Lopez. Roee Gutman. "Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas." Statist. Sci. 32 (3) 432 - 454, August 2017.


Published: August 2017
First available in Project Euclid: 1 September 2017

zbMATH: 06870254
MathSciNet: MR3696004
Digital Object Identifier: 10.1214/17-STS612

Keywords: Causal inference , Matching , multiple treatments , observational data , propensity score

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.32 • No. 3 • August 2017
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