title: Repair of Partly Misspecified Causal Diagrams.
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 subsequent causal inferences. Results based on simulated data and data derived from an observational cohort illustrate the potential for data-assisted elicitation in epidemiologic applications. See video abstract at, http://links.lww.com/EDE/B208.
authors.affiliation: From the aSchool of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia; bAustralian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, Parkville, VIC, Australia; cDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; and dCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Oates
authors.forename: Jessica
authors.initials: J
authors.lastname: Kasza
authors.forename: Julie A
authors.initials: JA
authors.lastname: Simpson
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Forbes
mesh_terms: D001315:Australia; D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012680:Sensitivity and Specificity