To estimate casual treatment effects, we propose a new matching approach
based on the reduced covariates obtained from sufficient dimension reduction.
Compared to the original covariates and the propensity score, which are
commonly used for matching in the literature, the reduced covariates are
estimable nonparametrically under a mild assumption on the original covariates,
and are sufficient and effective in imputing the missing potential outcomes.
Under the ignorability assumption, the consistency of the proposed approach
requires a weaker common support condition. Read More