Sensitivity analysis of the unconfoundedness assumption in observational studies

Xavier de Luna (xavier.deluna@stat.umu.se) and Mathias Lundin
Additional contact information
Xavier de Luna: Department of Statistics, Umeå University, Postal: S-901 87 Umeå, Sweden
Mathias Lundin: Department of Statistics, Umeå University, Postal: S 901 87 Umeå, Sweden

No 2009:12, Working Paper Series from IFAU - Institute for Evaluation of Labour Market and Education Policy

Abstract: In observational studies, the estimation of a treatment effect on an outcome of interest is often done by controlling on a set of pre-treatment characteristics (covariates). This yields an unbiased estimator of the treatment effect when the assumption of unconfoundedness holds, that is, there are no unobserved covariates affecting both the treatment assignment and the outcome. This is in general not realistically testable. It is, therefore, important to conduct an analysis about how sensitive the inference is with respect to the unconfoundedness assumption. In this paper we propose a procedure to conduct such a Bayesian sensitivity analysis, where the usual parameter uncertainty and the uncertainty due to the unconfoundedness assumption can be compared. To measure departures from the assumption we use a correlation coefficient which is intuitively comprehensible and ensures that the results of sensitivity analyses made on different evaluation studies are comparable. Our procedure is applied to the Lalonde data and to a study of the effect of college choice on income in Sweden.

Keywords: Causal inference; effects of college choice; propensity score; register data (search for similar items in EconPapers)
JEL-codes: C11 C15 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2009-06-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

Published as de Luna, Xavier and Mathias Lundin, 'Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings' in Journal of Applied Statistics, 2014, pages 1767-1784.

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