Working Paper Series,
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

JEL-codes: C11; C15

21 pages, June 10, 2009

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Xavier de Luna and Mathias Lundin, (2014), 'Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings', Journal of Applied Statistics, vol 41, no 8, pages 1767-1784

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