Estimating the average treatment effect under limited overlap via Polynomial Approximation and Extrapolation
有限重叠下平均处理效应的估计：多项式逼近与外推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：摘要直接研究观测数据中平均处理效应的估计，以及有限协变量重叠对因果效应推断的影响。
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
有限重叠下平均处理效应的估计：多项式逼近与外推
- 研究问题
- 当处理组与对照组的协变量重叠有限时，如何保留原始平均处理效应这一目标，同时改善逆概率加权估计与区间推断？
- 方法与思路
- 作者对一系列加权处理效应计算逆概率加权估计，利用目标随超参数变化的多项式结构拟合，再外推恢复平均处理效应。
- 材料陈述的贡献
- 摘要称，该估计量在比标准逆概率加权更弱的重叠条件下具有一致性和渐近正态性，并在模拟中改善估计及区间表现。
- 阅读关注点
- 应核对多项式表示是否精确、阶数与超参数如何选择，以及外推误差、倾向得分估计误差和重叠条件如何进入理论。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未给出具体识别假设、重叠条件与模拟数值，不能判断改善范围。
原文摘要
Estimating the average treatment effect (ATE) remains a fundamental challenge in observational studies in the presence of poor or limited covariate overlap. Although the inverse probability weighting (IPW) estimator is a widely used approach for estimating the ATE, its performance can deteriorate substantially when overlap is limited, often resulting in increased finite sample bias and unreliable confidence intervals. One common strategy is to shift attention from the original target estimand, the ATE, to alternative estimands such as a class of weighted ATEs that are less sensitive to extreme propensity scores; however, doing so changes the scientific question of interest. In this manuscript, we propose a novel ATE estimator that preserves the original target estimand, the ATE, while improving robustness to limited overlap. A key idea is that this class of estimands can be represented by a polynomial function of a hyperparameter characterizing the estimands. Exploiting this structure, the proposed method computes IPW estimators for a sequence of such estimands, models these estimates using a polynomial regression, and extrapolates to recover the ATE. We show that the estimator has consistency and asymptotic normality under weaker overlap conditions than required for the standard IPW estimator. Simulation studies demonstrate that the proposed method improves estimation accuracy and interval performance in settings with limited overlap. In addition to its theoretical and empirical advantages, the proposed approach has a clear interpretation and is easy to implement using standard statistical software.