Nonparametric identification of causal effects in clustered observational studies with differential selection
差异性选择下聚类观察研究因果效应的非参数识别AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确推导非随机群体处理和差异性选择下的因果效应识别及调整方法。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
差异性选择下聚类观察研究因果效应的非参数识别
- 研究问题
- 当处理非随机地赋予整个群体，且个体进入哪个群体取决于群体处理状态时，因果效应如何识别，常规调整方式是否仍适用？
- 方法与思路
- 作者区分存在和不存在差异性群体选择的设计，推导识别结果与相应调整变量集合，概述估计器，并结合模拟和两个应用考察偏差。
- 材料陈述的贡献
- 摘要称既有聚类观察研究隐含排除了差异性选择，而允许该机制后需要不同的调整集合；模拟和应用用于展示其潜在重要性。
- 阅读关注点
- 核对个体选择与群体处理的时间顺序、目标效应定义，以及两类设计所需的条件独立关系、调整集合和可观测信息。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出具体识别假设、调整变量或偏差数值，不能将结果推广至任意群体选择机制。
原文摘要
The clustered observational study (COS) design is the observational study counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In education, treatments may be given to all students within some schools but withheld from all students in other schools. In health studies, treatments may be applied to clusters such as hospitals or groups of patients treated by the same physician. In this manuscript, we study the identification of causal effects in clustered observational study designs. We focus on the prospect of differential selection of units to clusters, which occurs when the units' cluster selections depend on the clusters' treatment assignments. Extant work on COSs has made an implicit assumption that rules out the presence of differential selection. We derive the identification results for designs with differential selection and that contexts with differential cluster selection require different adjustment sets than standard designs. We outline estimators for designs with and without differential selection. Using a series of simulations, we outline the magnitude of the bias that can occur with differential selection. We then present two empirical applications focusing on the likelihood of differential selection.