Detecting multiple confounders

• Published in: Journal of Statistical Planning and Inference
• Main Authors: Wang, Xueli, Geng, Zhi, Chen, Hua, Xie, Xianchao
• Other Authors: Geng, Z (reprint author), Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China., Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China., Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China.
• Format: Journal/Newspaper
• Language: English
• Published: journal of statistical planning and inference 2009
• Subjects: Causal inference; Confounder; Confounding; COLLAPSIBILITY; DIAGRAMS; Greenland
• Online Access: https://hdl.handle.net/20.500.11897/246483; https://doi.org/10.1016/j.jspi.2008.06.013

This paper proposes an approach for detecting multiple confounders which combines the advantages of two Causal models, the Potential outcome model and the casual diagram. The approach need not use a complete Causal diagram as long as it is known that known covariate set Z contains the parent set of the exposure E. On the other a hand, whether a covariate is or not a confounder may depend on its categorization. We introduce uniform non-confounding which implies non-confounding in any subpopulation defined by the interval of a covariate (or any pooled level for a discrete covariate). We show that the conditions in Miettinen and Cook's criteria for non-confounding also imply uniform non-con founding. Further we present an algorithm for deleting non-confounders from the potential confounder set Z, which extends Greenland et al.'s [1999a. Causal diagrams for epidemiologic research. Epidemiology 10, 37-48] approach by splitting Z into a series of potential confounder subsets. We also discuss conditions for non-confounding bias in the subpopulations in which we are interested, where the subpopulations may be defined by non-con founders. (C) 2008 Elsevier B.V. All rights reserved. Statistics & Probability SCI(E) 0 ARTICLE 3 1073-1081 139