使用仪表变量来测量跨度滞后面板模型中的因果关系
Madhurbain Singh1,2,3, Brad Verhulst4, Philip Vinh1,2
1Department of Human and Molecular Genetics, Virginia Commonwealth University.
Multivariate behavioral research
|February 15, 2024
概括
本研究将仪器变量 (IV) 引入交叉滞后面板模型 (CLPMs),以改善因果推理. 新方法估计了远端和近端影响,提高了对面板数据中不同时间间隔的因果影响的检测.
科学领域:
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
- 生物统计学 生物统计学
背景情况:
- 交叉滞后面板模型 (CLPMs) 是分析纵向数据中的因果关系的标准.
- 随着评估间隔的延长,CLPMs检测滞后效应的能力会下降.
- 当前的方法在时间间隔很长时,难以推断因果关系.
研究的目的:
- 通过结合仪表变量 (IVs) 来增强CLPM中的因果推断.
- 开发一种建模策略,用于估计远端和近端效应.
- 为了解决影响滞后效应检测能力的时间间隔的限制.
主要方法:
- 将仪表变量 (IV) 集成到一个双波,双变量的CLPM框架中.
- 估计格兰杰因果 (远距离) 和同时 (近距离) 效应.
- 使用模拟和经验应用来验证拟议的模型.
主要成果:
- 拟议的IV-CLPM方法允许估计远端效应 (随时间衰变) 和近端效应 (随时间积累).
- 靠近效应为因果关系提供了关键的见解,当远距离效应由于长时间间隔变得无法检测时.
- 展示了时间间隔对因果推理的影响,并提出了克服这些局限性的策略.
结论:
- IV-CLPM提供了一种可靠的方法,用于在小组研究中推断因果关系,无论时间间隔如何.
- 显著的近距离效应和微不足道的远距离效应表明,对于标准的CLPM滞后效应估计,时间间隔可能过长.
- 突出使用遗传变量作为IV在大规模面板数据分析中的实用性和约束性.
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