title: Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder.
abstract: We consider the estimation of the causal effect of a binary exposure on a continuous outcome. Confounding and missing data are both likely to occur in practice when observational data are used to estimate this causal effect. In dealing with each of these problems, model misspecification is likely to introduce bias. We present augmented inverse probability weighted (AIPW) estimators that account for both confounding and missing data, with the latter occurring in a single variable only. These estimators have an element of robustness to misspecification of the models used. Our estimators require two models to be specified to deal with confounding and two to deal with missing data. Only one of each of these models needs to be correctly specified. When either the outcome or the exposure of interest is missing, we derive explicit expressions for the AIPW estimator. When a confounder is missing, explicit derivation is complex, so we use a simple algorithm, which can be applied using standard statistical software, to obtain an approximation to the AIPW estimator.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Australia. ewi@unimelb.edu.au
authors.forename: E J
authors.initials: EJ
authors.lastname: Williamson
authors.forename: A
authors.initials: A
authors.lastname: Forbes
authors.forename: R
authors.initials: R
authors.lastname: Wolfe
mesh_terms: D000758:Anesthesia / Q000379:methods; D015984:Causality; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D019370:Observation; D011336:Probability; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis