论文标题
在存在极端反比重量的存在下，因果推断的框架：重叠权重的作用
A framework for causal inference in the presence of extreme inverse probability weights: the role of overlap weights
论文作者
论文摘要
在本文中，我们考虑了当存在极高的概率权重时估计平均治疗效果的最新进展，并专注于解释可能违反阳性假设的方法。这些方法旨在估算对具有临床平衡的患者亚群的治疗效果。我们提出了一种系统的方法来确定其相关的因果估计，并开发出针对针对这种亚种群的权重的特性的新见解。然后，我们检查重叠重量，匹配权重，香农的熵重量和β重量的作用。这有助于我们从分析和模拟中表征和比较其基本估计量，从精度，精度和根平方误差方面。此外，我们研究了其增强估计器的渐近行为（模拟双重估计器），当正确指定倾向或回归模型时，这会导致改善的估计。根据分析和仿真结果，我们得出结论，总体重叠权重比匹配权重优于匹配权重，尤其是在中等或极端违反阳性假设的情况下。最后，我们使用以极端趋势权重标记的真实数据示例说明了这些方法。
In this paper, we consider recent progress in estimating the average treatment effect when extreme inverse probability weights are present and focus on methods that account for a possible violation of the positivity assumption. These methods aim at estimating the treatment effect on the subpopulation of patients for whom there is a clinical equipoise. We propose a systematic approach to determine their related causal estimands and develop new insights into the properties of the weights targeting such a subpopulation. Then, we examine the roles of overlap weights, matching weights, Shannon's entropy weights, and beta weights. This helps us characterize and compare their underlying estimators, analytically and via simulations, in terms of the accuracy, precision, and root mean squared error. Moreover, we study the asymptotic behaviors of their augmented estimators (that mimic doubly robust estimators), which lead to improved estimations when either the propensity or the regression models are correctly specified. Based on the analytical and simulation results, we conclude that overall overlap weights are preferable to matching weights, especially when there is moderate or extreme violations of the positivity assumption. Finally, we illustrate the methods using a real data example marked by extreme inverse probability weights.
