Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
加权推断：不同估计目标与重抽样机制下的残差化区间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：摘要明确研究观察性因果推断中协变量平衡权重及处理效应的区间推断。
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…
加权推断：不同估计目标与重抽样机制下的残差化区间
- 研究问题
- 观察性因果推断中加权回归的稳健标准误可能过于保守时，如何在不同估计目标和重抽样框架下改善区间精度？
- 方法与思路
- 作者建议在结果对处理的加权回归中加入平衡协变量及其处理交互项，通过残差化构造稳健标准误并分析不同抽样框架。
- 材料陈述的贡献
- 摘要称精确平衡时该标准误在多种框架下渐近正确且更精确；近似平衡时，参数模型增广也能通过缩小残差改善精度。
- 阅读关注点
- 应核对各抽样框架的估计目标、权重估计不确定性的处理，以及精确平衡和近似平衡下有效性结论的区别。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出完整条件，不能将精确平衡结论直接推广。
原文摘要
Weighting procedures are used in observational causal inference to adjust for covariate imbalance within the sample. Common practice for inference is to estimate robust standard errors from a weighted regression of outcome on treatment. However, it is well known that weighting can inflate variance estimates, sometimes significantly, leading to standard errors and confidence intervals that are overly conservative. We instead examine and recommend the use of robust standard errors from a weighted regression that additionally includes the balancing covariates and their interactions with treatment. We show that these standard errors are more precise and asymptotically correct for weights that achieve exact balance under multiple common resampling frameworks, including design-based and model-based inference, as well as superpopulation sampling with a finite sample correction. Gains to precision can be quite significant when the balancing weights adjust for prognostic covariates. For procedures that balance only approximately or in expectation, such as inverse propensity weighting or approximate balancing weights, our proposed method improves precision by reducing residuals through augmentation with the parametric model. We demonstrate our approach through simulation and re-analysis of multiple empirical studies.