标题： 使用逆概率加权的稳健推理 显示英文标题

标题： Robust Inference Using Inverse Probability Weighting

摘要： 逆概率加权（IPW）在经济学及其他学科的经验研究中被广泛使用。 由于在存在“小分母”的情况下，高斯近似表现不佳，因此修整通常被用作一种正则化策略。 然而，对观测值的随意修整使得通常的推断程序对于目标估计量无效，即使在大样本的情况下也是如此。 在本文中，我们首先展示，IPW 估计量可以具有不同的（高斯或非高斯）渐近分布，这取决于概率权重“接近于零”的程度以及修整阈值的大小。 作为补救措施，我们提出了一种推理程序，该程序不仅对进入 IPW 估计器的小概率权重具有鲁棒性，而且通过适应这些不同的渐近分布，还对广泛的修整阈值选择具有鲁棒性。 这种鲁棒性是通过采用重采样技术并纠正一个不可忽略的修整偏差来实现的。 我们还提出了一种易于实施的方法，通过最小化渐近均方误差的经验类比来选择修整阈值。 此外，我们证明了我们的推理程序在使用数据驱动的修整阈值时仍然有效。 我们通过重新审视来自国家支持工作计划的数据集来说明我们的方法。 显示英文摘要

摘要： Inverse Probability Weighting (IPW) is widely used in empirical work in economics and other disciplines. 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 first show that the IPW estimator can have different (Gaussian or non-Gaussian) asymptotic distributions, depending on how "close to zero" the probability weights are and on how large the trimming threshold is. As a remedy, 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, by adapting to these different asymptotic distributions. This robustness is achieved by employing resampling techniques and by correcting a non-negligible trimming bias. We also propose an easy-to-implement method for choosing the trimming threshold by minimizing an empirical analogue of the asymptotic mean squared error. In addition, we show that our inference procedure remains valid with the use of a data-driven trimming threshold. We illustrate our method by revisiting a dataset from the National Supported Work program.