关于检测两个变量之间的因果关系中的种群异质性:从双胞胎对中收集的数据的有限混合模型
Philip Vinh1, Brad Verhulst2, Conor V Dolan3
1Virginia Commonwealth University.
Research square
|July 23, 2024
概括
这项研究引入了一种新的模型,以了解精神疾病中复杂的因果关系. 它有助于确定并发症的主要原因,通过考虑个体差异来改进治疗策略.
科学领域:
- 精神病学是一个精神病学.
- 因果推理因果推理
- 量化心理学 量化心理学
背景情况:
- 因果推断依赖于诸如单向/双向因果关系和人口同质性的假设,这些假设往往被忽视.
- 精神病并发症很常见,疾病可能相互影响 (例如,导致物质使用的抑郁症,或者反之).
- 确定主要疾病对于有效的治疗计划至关重要.
研究的目的:
- 探索有限混合模型,以描绘因果关系中的样本内异质性.
- 将双胞胎数据的因果关系方向 (DoC) 模型扩展到混合物分布模型.
- 调查因果方向双胞胎混合 (mixCLPM) 模型在因果方向中检测和建模异质性的潜力.
主要方法:
- 利用有限混合模型来分析样本内的异质性.
- 将因果关系方向 (DoC) 模型扩展到混合物分配框架.
- 使用模拟来评估与双胞胎数据的因果关系双胞胎混合 (mixCLPM) 模型的方向.
主要成果:
- 拟议的混合模型允许计算替代因果方向的单个数据概率.
- 模拟表明混合CLPM模型能够检测和模拟来自不同因果方向的异质性.
- 该研究考虑了双胞胎数据中的四种可能的对对因果方向组合.
结论:
- 有限混合模型提供了一个强大的方法来解决因果推理中的人口异质性.
- 混合CLPM模型提供了一种强大的工具,用于解开精神病的复杂因果途径.
- 准确确定因果方向对于个性化精神病治疗至关重要.
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