Mixing Samples to Address Weak Overlap in Causal Inference
通过混合样本应对因果推断中的弱重叠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：摘要直接针对因果效应加权估计中的正值性、弱重叠与目标人群保持问题。
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…
关注这篇论文的作者
通过混合样本应对因果推断中的弱重叠
- 研究问题
- 观测研究中处理组与对照组重叠较弱时，如何降低加权估计的不稳定性，同时保留原目标参数和目标人群？
- 方法与思路
- 作者混合处理与对照单位构造合成处理组，使倾向分数收缩；以 M 估计构建 MIPW，并通过重抽样扩展到其他加权方法。
- 材料陈述的贡献
- 摘要称，混合方法可保持无偏并降低方差，不需删除极端观测或改变目标人群，且可适配熵平衡等加权方案。
- 阅读关注点
- 应核对混合样本如何恢复原目标参数、无偏和降方差结论的条件，以及重抽样、混合比例和倾向分数估计的作用。
- 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。摘要未明确弱重叠与完全无重叠的边界，也未列出无偏性的条件。
原文摘要
In observational studies, the assumption of sufficient overlap (positivity) is fundamental for the identification and estimation of causal effects. Failing to account for this assumption yields inaccurate and potentially infeasible estimators. To address this issue, we introduce a simple yet novel approach, \textit{mixing}, which mitigates overlap violations by constructing a synthetic treated group that combines treated and control units. Our strategy offers three key advantages. First, it improves the accuracy of the estimator by preserving unbiasedness while reducing variance. The benefit is particularly significant in settings with weak overlap, though the method remains effective regardless of the overlap level. This phenomenon results from the shrinkage of propensity scores in the mixed sample, which enhances robustness to poor overlap. Second, it enables direct estimation of the target estimand without discarding extreme observations or modifying the target population, thus facilitating a straightforward interpretation of the results. Third, the mixing approach is highly adaptable to various weighting schemes, including contemporary methods such as entropy balancing. The estimation of the Mixed IPW (MIPW) estimator is done via M-estimation, and the method extends to a broader class of weighting estimators through a resampling algorithm. We illustrate the mixing approach through extensive simulation studies and provide practical guidance with a real-data analysis.