Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
通过高维协变量平衡倾向得分稳健估计因果效应
Summary We propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. Our method consists of three steps. We first use a class of penalized $M$-estimators for the propensity score and outcome models. We then calibrate the initial estimate of the propensity score by balancing a carefully selected subset of covariates that are predictive of the outcome.…