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August 2010 Graphical Models for Inference Under Outcome-Dependent Sampling
Vanessa Didelez, Svend Kreiner, Niels Keiding
Statist. Sci. 25(3): 368-387 (August 2010). DOI: 10.1214/10-STS340


We consider situations where data have been collected such that the sampling depends on the outcome of interest and possibly further covariates, as for instance in case-control studies. Graphical models represent assumptions about the conditional independencies among the variables. By including a node for the sampling indicator, assumptions about sampling processes can be made explicit. We demonstrate how to read off such graphs whether consistent estimation of the association between exposure and outcome is possible. Moreover, we give sufficient graphical conditions for testing and estimating the causal effect of exposure on outcome. The practical use is illustrated with a number of examples.


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Vanessa Didelez. Svend Kreiner. Niels Keiding. "Graphical Models for Inference Under Outcome-Dependent Sampling." Statist. Sci. 25 (3) 368 - 387, August 2010.


Published: August 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1329.62042
MathSciNet: MR2791673
Digital Object Identifier: 10.1214/10-STS340

Keywords: Causal inference , Collapsibility , Odds ratios , selection bias

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.25 • No. 3 • August 2010
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