ABSTRACT

To estimate causal treatment effects, we propose a new matching approach based on the reduced covariates obtained from sufficient dimension reduction. Compared with the original covariates and the propensity score, which are commonly used for matching in the literature, the reduced covariates are nonparametrically estimable and are effective in imputing the missing potential outcomes, under a mild assumption on the low-dimensional structure of the data. Under the ignorability assumption, the consistency of the proposed approach requires a weaker common support condition. In addition, researchers are allowed to employ different reduced covariates to find matched subjects for different treatment groups. We develop relevant asymptotic results and conduct simulation studies as well as real data analysis to illustrate the usefulness of the proposed approach.

摘要

为了评估因果关系的治疗效果，我们基于从充分降维中获得的减少的协变量，提出了一种新的匹配方法。与文献中通常用于匹配的原始协变量和倾向得分相比，在对数据的低维结构进行温和假设的情况下，减少后的协变量是非参数估计的，并且可以有效地估算缺失的潜在结果。在可燃性假设下，所提出方法的一致性要求较弱的共同支持条件。此外，允许研究人员采用不同的简化协变量来查找不同治疗组的匹配受试者。