Propensity score matching and subclassification in observational studies with multi-level treatments.

Abstract:

:In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. 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 and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment 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.

journal_name

Biometrics

journal_title

Biometrics

authors

Yang S,Imbens GW,Cui Z,Faries DE,Kadziola Z

doi

10.1111/biom.12505

subject

Has Abstract

pub_date

2016-12-01 00:00:00

pages

1055-1065

issue

4

eissn

0006-341X

issn

1541-0420

journal_volume

72

pub_type

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