论文标题
重叠，匹配或熵权重：我们要加权什么？
Overlap, matching, or entropy weights: what are we weighting for?
论文作者
论文摘要
最近，使用逆概率权重（IPW）来处理缺乏足够积极性的统计方法激增。但是，这些新生的发展提出了许多问题。因此，我们证明了平衡估计器（重叠，匹配和熵权重）的能力，以处理缺乏阳性的能力。与IPW相比，已经证明平气估计器灵活且易于解释。但是，促进其广泛使用需要研究人员清楚地知道为什么，何时应用它们以及期望什么。 在本文中，我们提供了使用这些估计器来实现强大结果的理由。我们特别研究了治疗分配的影响可能对治疗效果的估计产生的影响。我们将IPW估计量的典型陷阱及其与平均治疗效应（ATT）和对照组（ATC）的估计量的关系归零。此外，我们还将IPW修剪与设备估计器进行了比较。我们特别关注两个要点：从根本上区分它们的估计是什么？我们什么时候应该期望类似的结果？我们的发现是通过蒙特卡洛模拟研究和有关医疗保健支出的数据示例来说明的。
There has been a recent surge in statistical methods for handling the lack of adequate positivity when using inverse probability weights (IPW). However, these nascent developments have raised a number of questions. Thus, we demonstrate the ability of equipoise estimators (overlap, matching, and entropy weights) to handle the lack of positivity. Compared to IPW, the equipoise estimators have been shown to be flexible and easy to interpret. However, promoting their wide use requires that researchers know clearly why, when to apply them and what to expect. In this paper, we provide the rationale to use these estimators to achieve robust results. We specifically look into the impact imbalances in treatment allocation can have on the positivity and, ultimately, on the estimates of the treatment effect. We zero into the typical pitfalls of the IPW estimator and its relationship with the estimators of the average treatment effect on the treated (ATT) and on the controls (ATC). Furthermore, we also compare IPW trimming to the equipoise estimators. We focus particularly on two key points: What fundamentally distinguishes their estimands? When should we expect similar results? Our findings are illustrated through Monte-Carlo simulation studies and a data example on healthcare expenditure.
