Abstract
PurposeTo show conditions of covariate balance for no confounding in the sufficient-cause model and discuss its relationship with exchangeability conditions. MethodsWe consider the link between the sufficient-cause model and the counterfactual model, emphasizing that the target population plays a key role when discussing these conditions. Furthermore, we incorporate sufficient causes within the directed acyclic graph framework. We propose to use each of the background factors in sufficient causes as representing a set of covariates of interest and discuss the presence of covariate balance by comparing joint distributions of the relevant background factors between the exposed and the unexposed groups. ResultsWe show conditions for partial covariate balance, covariate balance, and full covariate balance, each of which is stronger than partial exchangeability, exchangeability, and full exchangeability, respectively. This is consistent with the fact that the sufficient-cause model is a “finer” model than the counterfactual model. ConclusionsCovariate balance is a sufficient, but not a necessary, condition for no confounding irrespective of the target population. Although our conceptualization of covariate imbalance is closely related to the recently proposed counterfactual-based definition of a confounder, the concepts of covariate balance and confounder should be clearly distinguished.
Submitted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
