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February 1999 Confounding and Collapsibility in Causal Inference
Sander Greenland, Judea Pearl, James M. Robins
Statist. Sci. 14(1): 29-46 (February 1999). DOI: 10.1214/ss/1009211805

Abstract

Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding in experimental designs, the topic is given little or no discussion in most statistics texts. We here provide an overview of confounding and related concepts based on a counterfactual model for causation. Special attention is given to definitions of confounding, problems in control of confounding, the relation of confounding to exchangeability and collapsibility, and the importance of distinguishing confounding from noncollapsibility.

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Sander Greenland. Judea Pearl. James M. Robins. "Confounding and Collapsibility in Causal Inference." Statist. Sci. 14 (1) 29 - 46, February 1999. https://doi.org/10.1214/ss/1009211805

Information

Published: February 1999
First available in Project Euclid: 24 December 2001

zbMATH: 1059.62506
Digital Object Identifier: 10.1214/ss/1009211805

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.14 • No. 1 • February 1999
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