Propensity Score Matching and Subclassification in Observational Studies with Multi-level Treatments

Shu Yang, Guido Imbens, Zhanglin Cui, Douglas E. Faries and Zbigniew Kadziola
Additional contact information
Shu Yang: Harvard University
Zhanglin Cui: Eli Lilly and Company
Douglas E. Faries: Eli Lilly and Company
Zbigniew Kadziola: Eli Lilly and Company

Research Papers from Stanford University, Graduate School of Business

Abstract: In this paper, we develop new methods for estimating average treatment effects in observational studies, focusing on settings with more than two treatment levels under unconfoundedness given pre-treatment variables. We emphasize subclassification and matching methods which have been found to be effective in the binary treatment literature and which are among the most popular methods in that setting. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness, that adjusting for or matching on a scalar function of the pre-treatment variables removes all biases associated with observed pre-treatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.

Date: 2015-12
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Journal Article: Propensity score matching and subclassification in observational studies with multi‐level treatments (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3381

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