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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

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Selective Ignorability Assumptions in Causal Inference

Marshall M. Joffe / Wei Peter Yang / Harold I. Feldman
Published Online: 2010-03-05 | DOI: https://doi.org/10.2202/1557-4679.1199

Most attempts at causal inference in observational studies are based on assumptions that treatment assignment is ignorable. Such assumptions are usually made casually, largely because they justify the use of available statistical methods and not because they are truly believed. It will often be the case that it is plausible that conditional independence holds at least approximately for a subset but not all of the experience giving rise to one's data. Such selective ignorability assumptions may be used to derive valid causal inferences in conjunction with structural nested models. In this paper, we outline selective ignorability assumptions mathematically and sketch how they may be used along with otherwise standard G-estimation or likelihood-based methods to obtain inference on structural nested models. We also consider use of these assumptions in the presence of selective measurement error or missing data when the missingness is not at random. We motivate and illustrate our development by considering an analysis of an observational database to estimate the effect of erythropoietin use on mortality among hemodialysis patients.

Keywords: causal inference; ignorability; end-stage renal disease; anemia

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Published Online: 2010-03-05

Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1199.

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©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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