Testing for the Unconfoundedness Assumption Using an Instrumental Assumption

The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assum...

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Bibliographic Details
Published in:Journal of causal inference 2014-09, Vol.2 (2), p.187-199
Main Authors: de Luna, Xavier, Johansson, Per
Format: Article
Language:English
Subjects:
average treatment effect
causal inference
job practice
nonparametric identification
Statistics
statistik
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Summary:The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.
ISSN:2193-3677
2193-3685
2193-3685
DOI:10.1515/jci-2013-0011