在依赖的因果方向中检测异质性:基于模型的递归分区方法
Wolfgang Wiedermann1, Bixi Zhang2, Dexin Shi3
1Statistics, Measurement, and Evaluation in Education, Department of Educational, School, and Counseling Psychology, College of Education and Human Development, Missouri Prevention Science Institute, University of Missouri, 13A Hill Hall, Columbia, MO, 65211, USA. wiedermannw@missouri.edu.
这项研究引入了一种新的方法,以找到具有不同因果关系的子组,超越普遍因果效应的假设. 它结合了分区和非高斯发现,在多样化的群体中改进了因果分析.
科学领域:
- 因果推理的原因推理.
- 机器学习 机器学习
- 统计建模 统计建模
背景情况:
- 因果发现方法通常假设因果效应在不同人群中的同质性.
- 亚种群内的因果机制变化可能导致不准确的全球因果结构.
- 识别异构的因果关系对于强大的科学理解至关重要.
研究的目的:
- 开发一种用于识别具有明显因果机制的子群的算法.
- 将基于模型的递归分区与非高斯因果发现相结合.
- 在现有的因果发现技术中解决因果效应同质性的限制.
主要方法:
- 集成基于模型的递归分区,用于子组识别.
- 非高斯因果发现的应用,以检测不同的因果方向和大小.
- 合成数据的分析,以在不同条件下评估算法性能.
主要成果:
- 拟议的算法成功地识别了具有不同因果效应的子群.
- 通过大效果大小和大量样本大小来提高性能.
- 在数字认知的现实世界案例研究中证明了可行性.
结论:
- 该方法有效地检测出因果异质的子组,为因果关系提供了更细致的理解.
- 研究结果表明,大样本大小和显著效果大小可以提高对抗因果子组的检测.
- 该方法为探索不同人群因果机制的变异提供了有价值的工具.
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