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### 基于协变量平衡加权的平均处理效应的稳健有效估计

• 出版日期:2020-04-25 发布日期:2020-04-15

### A Robust and Efficient Estimation of Average Treatment Effects Based on Covariate Balance Weighting

Wu Hao & Peng Fei

• Online:2020-04-25 Published:2020-04-15

Abstract: Propensity score is a useful approach in estimating average treatment effects. However, the imbalance of covariate distribution between treatment group and control group usually leads to the extreme propensity score, i.e. some propensity scores will be very close to 0 or 1, which makes the ignorable assumption of causal inference near to false, and brings large bias and variance in the estimation of average treatment effects. Li et al. (2018a) advocate covariate balance weighting method to realize weighted balance of covariate distribution under the assumption of unconfoundedness, which resolves the impact by the extreme propensity scores. Based on the covariate balance weighting method, this article propose a more robust and efficient method, and reduces the trouble of model misspecification by super learner algorithm. Furthermore, we generalize the former method to model-free situations, which is also a doubly robust and efficient estimator. In Monte-Carlo simulation, the two proposed methods both have very small bias and variance when both outcome regression model and propensity score model are misspecified. We use the two methods in right heart catheterization data, and find that right heart catheterization will increase mortality by 6.3%.