标题： 基于逆概率加权的稳健推断 显示英文标题

标题： Robust Inference Using Inverse Probability Weighting

摘要： 逆概率加权（IPW）在项目评估和其他实证经济学应用中被广泛使用。 由于在存在“小分母”的情况下高斯近似表现不佳，修剪通常被用作一种正则化策略。 然而，随意修剪观测值会使目标估计量的常规推断程序在大样本中失效。 在本文中，我们提出了一种推断程序，该程序不仅对进入IPW估计器的小概率权重具有鲁棒性，而且对广泛的修剪阈值选择也具有鲁棒性。 我们的推断程序采用了一种新颖的偏差校正技术进行重采样。 具体而言，我们证明了IPW和修剪后的IPW估计量可能有不同的（高斯或非高斯）极限分布，这取决于概率权重接近零的程度以及修剪阈值。 通过适应这些不同的极限分布，我们的方法为目标估计量提供了更稳健的推断。 这种稳健性部分是通过校正一个不可忽略的修剪偏差来实现的。 我们在模拟研究中展示了我们方法的有限样本准确性，并通过重新审视国家支持工作计划的数据集来说明其应用。 显示英文摘要

摘要： Inverse Probability Weighting (IPW) is widely used in program evaluation and other empirical economics applications. As Gaussian approximations perform poorly in the presence of "small denominators," trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target estimand, even in large samples. In this paper, we propose an inference procedure that is robust not only to small probability weights entering the IPW estimator, but also to a wide range of trimming threshold choices. Our inference procedure employs resampling with a novel bias correction technique. Specifically, we show that both the IPW and trimmed IPW estimators can have different (Gaussian or non-Gaussian) limiting distributions, depending on how "close to zero" the probability weights are and on the trimming threshold. Our method provides more robust inference for the target estimand by adapting to these different limiting distributions. This robustness is partly achieved by correcting a non-negligible trimming bias. We demonstrate the finite-sample accuracy of our method in a simulation study, and we illustrate its use by revisiting a dataset from the National Supported Work program.