Wiedermann Wolfgang, Zhang Bixi, Shi Dexin
Statistics, 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.
Graduate Center, City University of New York, New York, NY, USA.
Behav Res Methods. 2024 Apr;56(4):2711-2730. doi: 10.3758/s13428-023-02253-8. Epub 2023 Oct 19.
Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.
用于评估变量关系因果属性的因果发现方法和依赖方向已经历了快速发展。然而，大多数因果发现方法依赖于因果效应同质性的假设，即所确定的因果结构预计对整个人口都成立。由于因果机制可能因亚群体而异，我们建议结合基于模型的递归划分方法和非高斯因果发现方法来识别此类亚群体。所得算法可以在温和的参数不等式假设下发现效应大小和因果方向可能不同的亚群体。描述了可行性条件，并给出了合成数据实验的结果，表明大效应和大样本量有利于检测具有可接受统计性能的因果竞争亚组。在一个实际数据示例中，说明了在数字认知发展背后的因果机制方面存在差异的有意义亚组的提取。讨论了潜在的扩展和最佳实践应用的建议。
