带有远端结果的因果隐性类分析:使用反向倾向权重的修改后的三步方法
Trà T Lê1, Felix J Clouth1, Jeroen K Vermunt1
1Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.
Multivariate behavioral research
|July 22, 2024
概括
本研究引入了新的倾向评分方法,以使用观察数据估计潜在类成员对结果的因果影响. 这些新方法提供了公正的估计,优于现有技术,但需要仔细考虑小样本大小.
科学领域:
- 统计 统计 统计 统计
- 因果推理因果推理
- 隐藏类分析 隐藏类分析
背景情况:
- 隐性类 (LC) 分析被广泛用于将类成员与结果联系起来.
- 从观测数据中估计因果效应需要因果推断技术,因为LC会员是非随机的.
- 现有的使用倾向分数的阶段性LC分析在因果效应估计方面存在局限性.
研究的目的:
- 提出和评估两种基于倾向分数的新策略,用于估计隐性阶级成员身份对远程结果的因果关系.
- 用观察数据解决隐性类分析中的混问题.
- 为了比较拟议方法的性能与现有的倾向性得分方法在阶段性隐性类分析.
主要方法:
- 修改偏差调整的三步隐性类分析,将倾向性得分纳入最后一步.
- 提出了两种策略:反向倾向权重 (IPW) 和包括倾向得分作为控制变量.
- 使用BCH或ML纠正处理分类错误;通过模拟和现实数据 (LISS面板) 评估性能.
主要成果:
- 两种拟议的方法都产生了基本上无偏见的参数估计,超过了之前建议的方法.
- 基于IPW的方法显示了较小样本大小的高可变性和潜在的非趋同.
- 这些方法使用LISS小组的数据成功地展示了这些方法.
结论:
- 新型倾向得分策略有效地估计了潜伏类成员对远程结果的因果关系.
- 这些方法为使用观测数据进行潜在类分析的因果推理提供了宝贵的进步.
- 研究人员在采用基于IPW的策略时应注意样本大小的限制.
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