Affinely invariant matching methods with ellipsoidal distributions
Consider the treated and control populations of size \(N_ t\) and \(N_ c\) respectively and \(X\) a set of matched variables recorded on all \(N_ t+N_ c\) units. Due to cost considerations, outcomes and additional covariates are recorded for matched subsamples of sizes \(n_ t\leq N_ t\) and \(n_ c\leq N_ c\) chosen such that the distributions of \(X\) among the \(n_ t\) and \(n_ c\) matched units are more similar than for random subsamples. The standard matched sample estimator of the treatment's effect on an outcome \(Y\) is \(\bar Y_ t-\bar Y_ c\) based on matched units. The bias of this estimator is less compared to the difference based on random subsamples. Consider ellipsoidal distributions for which there exists a linear transformation of the variables that results in a spherically symmetric distribution for the transformed variables. Matching methods based on population or sample inner products, such as discriminant matching or Mahalanobis metric matching or methods using propensity scores based on logistic regression estimators which are called affinely invariant, are used with ellipsoidal distributions. Furthermore, canonical forms for conditionally ellipsoidal distributions using conditionally affinely invariant matching methods are considered.
conditionally affinely invariant matching methods
scientific article; zbMATH DE number 64519