Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls
高维控制变量下条件平均处理效应的双重稳健推断
Plausible identification of conditional average treatment effects (CATEs) may rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second stage CATE estimator are not doubly-robust. Using…