使用支向量机器估计平均处理效果
Alexander Tarr1, Kosuke Imai1,2,3
1Institute for Quantitative Social Science, Harvard University, Massachusetts, USA.
Statistics in medicine
|February 7, 2025
概括
支持向量机 (SVM) 有效平衡因果效应估计的共变量. 这种机器学习方法优化了治疗和控制组平衡,同时最大化样本大小,超越现有技术.
科学领域:
- 机器学习 机器学习
- 因果推理因果推理
- 统计建模 统计建模
背景情况:
- 支持向量机 (SVM) 是一个广泛使用的分类算法.
- 估计因果关系需要在治疗组和对照组之间平衡共变量.
- 现有的共变量平衡方法存在局限性.
研究的目的:
- 为了证明SVM对共变量平衡和因果效应估计的实用性.
- 为了改进平衡和样本大小,将SVM作为基于内核的权重程序进行调整.
- 分析由SVM的规范化参数控制的共变量平衡和有效样本大小之间的权衡.
主要方法:
- 将SVM分类器调整为基于内核的权重程序.
- 尽量减少治疗组和对照组之间的最大平均差异.
- 使用SVM的规范化参数最大化有效样本大小,用于平衡样本大小边界计算.
主要成果:
- 在无证的情况下,SVM有效地平衡协变量并估计平均因果关系.
- SVM 提供了最大平衡子集问题的连续放松,并将其链接到枢密度匹配.
- 在SVM中的规范化参数控制了因果效应估计中的偏差差异权衡.
结论:
- 提出的基于SVM的方法与最先进的共变量平衡方法具有竞争力.
- SVM提供了一种灵活的方法来平衡共变量和估计因果关系.
- 模拟和经验研究验证了SVM程序的性能.
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