On estimating regression-based causal effects using sufficient dimension reduction
本文提出一种基于充分降维的回归因果效应估计量，该估计量比倾向得分方法需要的共同支撑条件更弱，且能超有效估计平均因果效应，模拟验证了其有限样本性质。
In many causal inference problems the parameter of interest is the regression causal effect, defined as the conditional mean difference in the potential outcomes given covariates. In this paper we discuss how sufficient dimension reduction can be used to aid causal inference, and we propose a new estimator of the regression causal effect inspired by minimum average variance estimation. The estimator requires a weaker common support condition than propensity score-based approaches, and can be used to estimate the average causal effect, for which it is shown to be asymptotically super-efficient. Its finite-sample properties are illustrated by simulation.