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.

翻译：近年来，为应对使用逆概率加权（IPW）时缺乏充分阳性（positivity）而提出的统计方法大量涌现。然而，这些新兴进展引发了一系列问题。因此，我们展示了均衡估计量（重叠权重、匹配权重和熵权重）处理阳性不足的能力。与IPW相比，均衡估计量已被证明灵活且易于解释。但推广其广泛应用要求研究者明确知晓为何使用、何时应用以及预期结果。本文提供了使用这些估计量以获得稳健结果的依据，具体探讨了治疗分配不平衡对阳性条件乃至治疗效应估计的影响。我们聚焦于IPW估计量的典型缺陷，及其与平均处理效应（ATT）和对照处理效应（ATC）估计量的关系。此外，我们还比较了IPW截断与均衡估计量。特别关注两个关键问题：这些估计量的目标参数（estimands）根本区别何在？何时应预期得到相似结果？通过蒙特卡洛模拟研究和医疗支出数据示例验证了我们的发现。