Balancing and Elimination of Nuisance Variables

Noorbaloochi Siamak, Nelson David and Asgharian Masoud
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Noorbaloochi Siamak: Minneapolis VA Medical Center and University of Minnesota
Nelson David: Minneapolis VA Medical Center and University of Minnesota
Asgharian Masoud: McGill University

The International Journal of Biostatistics, 2010, vol. 6, issue 2, 22

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.

Keywords: confounding; dimension reduction; sufficient summary; ancillarity (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.2202/1557-4679.1209

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