title: Balancing and elimination of nuisance variables.
abstract: Addressing covariate imbalance in causal analysis will be reformulated as an elimination of the nuisance variables problem. We show, within a counterfactual balanced setting, how averaging, conditioning, and marginalization techniques can be used to reduce bias due to a possibly large number of imbalanced baseline confounders. The notions of X-sufficient and X-ancillary quantities are discussed and, as an example, we show how sliced inverse regression and related methods from regression theory that estimate a basis for a central sufficient subspace provide alternative summaries to propensity based analysis. Examples for exponential families and elliptically symmetric families of distributions are provided.
authors.affiliation: Minneapolis VA Medical Center and University of Minnesota, USA.
authors.forename: Siamak
authors.initials: S
authors.lastname: Noorbaloochi
authors.forename: David
authors.initials: D
authors.lastname: Nelson
authors.forename: Masoud
authors.initials: M
authors.lastname: Asgharian
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D015233:Models, Statistical; D012107:Research Design