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Using causal diagrams to guide analysis in missing data problems

Daniel, Rhian ORCID:, Kenward, Michael G, Cousens, Simon N and De Stavola, Bianca L 2012. Using causal diagrams to guide analysis in missing data problems. Statistical Methods in Medical Research 21 (3) , pp. 243-256. 10.1177/0962280210394469

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Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR)) and the circumstances in which causal effects can be estimated without bias by analysing only the subjects with complete data. In doing so, we formally extend the back-door criterion of Pearl and others for use in incomplete data examples. These ideas are illustrated with an example drawn from an occupational cohort study of the effect of cosmic radiation on skin cancer incidence.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: Causal diagram; Causal inference; Missing data
Publisher: SAGE Publications
ISSN: 0962-2802
Last Modified: 03 Nov 2022 09:48

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