Journal article

Repair of Partly Misspecified Causal Diagrams

Chris J Oates, Jessica Kasza, Julie A Simpson, Andrew B Forbes

EPIDEMIOLOGY | LIPPINCOTT WILLIAMS & WILKINS | Published : 2017

Abstract

Errors in causal diagrams elicited from experts can lead to the omission of important confounding variables from adjustment sets and render causal inferences invalid. In this report, a novel method is presented that repairs a misspecified causal diagram through the addition of edges. These edges are determined using a data-driven approach designed to provide improved statistical efficiency relative to de novo structure learning methods. Our main assumption is that the expert is "directionally informed," meaning that "false" edges provided by the expert would not create cycles if added to the "true" causal diagram. The overall procedure is cast as a preprocessing technique that is agnostic to..

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Grants

Awarded by Australian National Health and Medical Research Council Centre of Excellence


Awarded by Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship


Funding Acknowledgements

J.K. is supported by Australian National Health and Medical Research Council Centre of Excellence Grant 1035261, awarded to the Victorian Centre for Biostatistics (ViCBiostat). J.A.S. is funded by an Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship 1104975.