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在违反正面性假设的情况下,倾向性得分权重方法的教程

Yi Liu^1,2, Yuan Wang³, Ying Gao³

¹Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

Statistics in medicine

|November 27, 2025

概括

倾向性评分权重方法通过关注加权平均治疗效应 (WATE,WATT,WATC) 来解决无法识别的因果关系. 本教程指导用户选择目标估计和使用ChiPS R包实现这些方法.

科学领域:

因果推理的原因推理.
生物统计学生物统计学
流行病学流行病学

背景情况:

传统的因果估计如ATE,ATT和ATC可能无法识别,因为违反了积极性假设.
权重对应方 (WATE,WATT,WATC) 提供了可识别的替代因果估计.
倾向得分 (PS) 方法对于解决观察性研究中的混至关重要.

研究的目的:

提供最近在倾向性得分 (PS) 权重方法方面的进展的全面审查.
提供关于选择目标估计数,实施PS加权估计器和进行诊断评估的实际指导.
引入ChiPS R包,以促进PS加权的因果推断.

主要方法:

审查最近的倾向性得分 (PS) 权衡方法.
关于选择主要目标估计和补充分析的指导.
实施PS加权估计器和加权后诊断程序.
使用广泛的模拟研究和两个现实世界的案例研究进行演示.

主要成果:

这项研究证明了PS权重对获得可识别的因果关系效应的有用性.
模拟研究证实了各种PS加权估计器的相关性和性能.
案例研究说明了这些方法在公共卫生研究中的应用.

结论:

倾向性得分权重为因果推理提供了一个强大的框架,当正面性被侵犯时.
ChiPS R套件增强了这些先进方法的实际应用.
这种方法可以在观察数据中对治疗效应进行有效估计,从而有助于内部有效性.

关键词:

有关因果推理的推理.重叠重量重量重叠重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量重量积极的积极性正面的积极性目标人口的目标人口.没有证据的无根据性.治疗效果的加权平均值.

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