Noorbaloochi Siamak, Nelson David, Asgharian Masoud
Minneapolis VA Medical Center and University of Minnesota, USA.
Int J Biostat. 2010;6(2):Article 6. doi: 10.2202/1557-4679.1209.
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.
在因果分析中解决协变量不平衡问题将被重新表述为消除干扰变量问题。我们展示了在反事实平衡设定下，如何使用平均、条件设定和边缘化技术来减少由于可能大量不平衡的基线混杂因素导致的偏差。讨论了X-充分量和X-辅助量的概念，并且作为一个例子，我们展示了切片逆回归和回归理论中的相关方法（这些方法估计中心充分子空间的一个基）如何为基于倾向得分的分析提供替代的汇总。还提供了指数族和椭圆对称分布族的例子。
