Balancing and elimination of nuisance variables

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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.

Original languageEnglish (US)
Article number6
JournalInternational Journal of Biostatistics
Volume6
Issue number2
DOIs
StatePublished - 2010

Keywords

  • Ancillarity
  • Confounding
  • Dimension reduction
  • Sufficient summary

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