In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap. Our proposed estimator relies on trimming observations with extreme propensity scores and uses a bias correction device for trimming bias. Our framework accommodates many research designs, such as unconfoundedness, local treatment effects, and difference-in-differences. Simulation exercises illustrate that our proposed tools indeed have attractive finite sample properties, which are aligned with our theoretical asymptotic results.

翻译：本文推导了一类新的治疗效应估计量的双稳健估计方法，该方法对协变量弱重叠也具有稳健性。我们提出的估计量依赖于剔除具有极端倾向得分的观测值，并采用偏差校正机制来修正截断偏差。该框架适用于多种研究设计，例如无混淆性、局部处理效应以及双重差分法。模拟实验表明，我们提出的工具确实具有良好的有限样本性质，这与我们的理论渐近结果一致。