发表机构
Tokyo Medical University; Yale School of Public Health（东京医科大学; 耶鲁大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对观察性研究中协变量重叠度有限时平均处理效应估计的问题，提出一种通过多项式近似与外推的新颖IPW类估计量，其在更弱重叠条件下具有一致性和渐近正态性，能提升估计精度与区间性能，且易实现。
AI 中文摘要
在协变量重叠度较差或有限的观察性研究中，估计平均处理效应（ATE）仍是一项基本挑战。尽管逆概率加权（IPW）估计量是估计ATE的常用方法，但当重叠度有限时，其性能会大幅下降，通常导致有限样本偏差增大和置信区间不可靠。一种常见策略是将关注焦点从原始目标估计量ATE转移到对极端倾向得分敏感性较低的替代估计量上，但这样做会改变所关注的科学问题。在本文中，我们提出了一种新颖的ATE估计量，该估计量保留了原始目标估计量ATE，同时提高了对有限重叠度的鲁棒性。一个核心思路是，一类估计量可表示为表征该类估计量的超参数的多项式函数。利用这一结构，所提方法计算一系列此类估计量的IPW估计值，用多项式函数对这些估计值进行建模，并通过外推来恢复ATE。我们证明，与标准IPW估计量所需的重叠度条件相比，该估计量在更弱的重叠度条件下具有一致性和渐近正态性。模拟研究表明，在重叠度有限的场景中，所提方法提高了估计精度和区间性能。除了理论和经验优势外，所提方法具有清晰的解释性，且可使用标准统计软件轻松实现。
英文摘要
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.
CommentsCausal inference, Inverse probability weighting, Positivity, Propensity score, Weighted average treatment effect, Polynomial regression
