交叉滞后网络模型不能证明因果关系,可以通过三角测量来评估.
Kimmo Sorjonen1, Bo Melin1, Gustav Nilsonne2
1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Acta psychologica
|September 13, 2025
概括
交叉滞后面板网络 (CLPN) 模型可能会从相关数据中产生虚假的因果结果. 研究人员应该使用三角测量来验证CLPN结果,并避免过度解释效应,特别是在心理健康研究中.
科学领域:
- 心理测量 心理测量 心理测量
- 网络分析 网络分析
- 因果推理因果推理
背景情况:
- 交叉滞后面板网络 (CLPN) 模型扩展了交叉滞后面板模型 (CLPM),用于分析两个时间点之间的变量之间的众多影响.
- 尽管关于从相关数据推断因果关系的争论仍在进行,但CLPN的发现经常被解释为因果关系.
研究的目的:
- 对非实验数据应用时,研究CLPN模型中虚假发现的潜力.
- 证明三角测定作为一种方法,以减轻CLPN分析中的错误阳性.
主要方法:
- 进行了模拟,以评估在有或没有直接因果影响的条件下CLPN的性能.
- 应用了之前提出的三角测量方法,重新分析了有关抑郁和焦虑症状的现有数据.
主要成果:
- 模拟显示,即使没有直接效应,CLPN也可以检测到交叉滞后效应,这表明存在虚假发现的风险.
- 使用三角测量重新分析表明,最初观察到的抑郁和焦虑症状之间的积极交叉滞后效应可能是虚假的.
结论:
- 基于非实验数据的CLPN发现不应被因果解释,因为存在错误阳性结果的风险.
- 三角测量作为一种有价值的技术来仔细检查CLPN结果,并减少错误因果推断的可能性.
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