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Visa Ämnen

Sensitivity analysis of the unconfoundedness assumption in observational studies

Abstract of Working paper 2009:12

In observational studies, the estimation of a treatment effect on an outcome of interestis oftendoneby controlling ona set ofpre-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 inferenceis with respect to the unconfoundedness assumption. In this paper we propose a procedure to conduct such aBayesian sensitivity analysis, wherethe usualparameter 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

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


Published by:

Jörgen Moen

Changed:

6/7/2010