分布重叠对广义界精度增益的作用
The Role of Distributional Overlap on the Precision Gain of Bounds for Generalization
摘要：
在过去的十年里，倾向得分方法对改善研究的概括性做出了重要贡献，这些研究不是从推理群体中随机选择样本。然而，这些方法需要假设，最近的工作已经考虑了边界方法的作用，提供了一系列与可观察数据一致的治疗影响估计。边界估计的一个重要限制是它们可能是无信息的宽。这激发了关于使用倾向得分分层来缩小界限的研究。本文评估了倾向得分中分布重叠对分层收紧界限的有效性的作用。使用两个模拟研究和两个案例研究的结果，我评估分布重叠和精度增益之间的关系，并讨论了当倾向得分分层作为一种方法，以提高精度的边界框架的影响。
英文摘要：
Over the past ten years, propensity score methods have made an important contribution to improving generalizations from studies that do not select samples randomly from a population of inference. However, these methods require assumptions and recent work has considered the role of bounding approaches that provide a range of treatment impact estimates that are consistent with the observable data. An important limitation to bound estimates is that they can be uninformatively wide. This has motivated research on the use of propensity score stratification to narrow bounds. This article assesses the role of distributional overlap in propensity scores on the effectiveness of stratification to tighten bounds. Using the results of two simulation studies and two case studies, I evaluate the relationship between distributional overlap and precision gain and discuss the implications when propensity score stratification is used as a method to improve precision in the bounding framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
