摘要翻译：
逆概率加权(IPW)在经济学等学科的实证工作中有着广泛的应用。由于高斯近似在“小分母”的情况下表现不佳，修剪通常被用作正则化策略。然而，对观测量的临时修剪使得通常的推断程序对目标估计无效，即使在大样本中也是如此。在本文中，我们首先证明了IPW估计器可以具有不同的（高斯或非高斯）渐近分布，这取决于概率权重的“接近零”程度和修剪阈值的大小。作为补救，我们提出了一个推理过程，通过适应这些不同的渐近分布，它不仅对进入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。这种鲁棒性是通过使用重采样技术和通过校正不可忽略的微调偏置来实现的。我们还提出了一种易于实现的方法，通过最小化渐近均方误差的经验模拟来选择修剪阈值。此外，我们还证明了在使用数据驱动的修剪阈值的情况下，我们的推理过程仍然有效。我们通过重新审视国家支持的工作计划中的一个数据集来说明我们的方法。
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英文标题：
《Robust Inference Using Inverse Probability Weighting》
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作者：
Xinwei Ma and Jingshen Wang
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1810.11397