Open Access
February 2010 Assumptions of IV Methods for Observational Epidemiology
Vanessa Didelez, Sha Meng, Nuala A. Sheehan
Statist. Sci. 25(1): 22-40 (February 2010). DOI: 10.1214/09-STS316


Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some rely on more restrictive parametric assumptions than others. We therefore discuss and contrast the most common IV approaches with relevance to typical applications in observational epidemiology. Further, we illustrate and compare the asymptotic bias of these IV estimators when underlying assumptions are violated in a numerical study. One of our conclusions is that all IV methods encounter problems in the presence of effect modification by unobserved confounders. Since this can never be ruled out for sure, we recommend that practical applications of IV estimators be accompanied routinely by a sensitivity analysis.


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Vanessa Didelez. Sha Meng. Nuala A. Sheehan. "Assumptions of IV Methods for Observational Epidemiology." Statist. Sci. 25 (1) 22 - 40, February 2010.


Published: February 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1328.62587
MathSciNet: MR2741813
Digital Object Identifier: 10.1214/09-STS316

Keywords: Causal inference , instrumental variables , Mendelian randomization , relative bias , structural mean models

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.25 • No. 1 • February 2010
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