Testing for the Unconfoundedness Assumption Using an Instrumental Assumption
2014 (English)In: Journal of Causal Inference, ISSN (Online) 2193-3685, (Print) 2193-3677, Vol. 2, no 2, 187-199 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Walter de Gruyter GmbH , 2014. Vol. 2, no 2, 187-199 p.
causal inference, average treatment effect, job practice, nonparametric identification
Probability Theory and Statistics
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-87968DOI: 10.1515/jci-2013-0011OAI: oai:DiVA.org:umu-87968DiVA: diva2:712686
FunderSwedish Research Council