通过局部图表进行因果结构学习
Wenyu Chen1, Mathias Drton2, Ali Shojaie3
1Department of Statistics, University of Washington, Seattle, WA 98195.
概括
本研究介绍了局部快速因果推理 (lFCI),这是一种用于学习因果结构的新算法. 它有效地处理复杂的网络与未测量的混因子和选择偏差,优于现有方法.
科学领域:
- 因果推理因果推理
- 机器学习 机器学习
- 网络分析 网络分析
背景情况:
- 学习因果结构在高维数据中具有挑战性,具有未测量的混因子和选择偏差.
- 现有的方法与包含枢纽节点的现实世界网络作斗争.
研究的目的:
- 开发一种新的因果结构学习算法,局部快速因果推理 (lFCI).
- 为了解决标准算法的局限性,在稀疏的,高维的设置与潜伏和选择变量.
主要方法:
- 为复杂的网络结构量身定制的稀疏性提出了一个新的本地概念.
- 开发了本地FCI (lFCI) 算法,这是快速因果推理算法的变体.
- 引入了条件依赖关系的局部确定假设.
主要成果:
- 在新的稀缺性条件和局部依赖性假设下,lFCI显示出一致性.
- 与标准FCI相比,该算法提供了较低的计算和样本复杂性.
- lFCI在大型随机网络上实现了最先进的性能,特别是那些有枢纽节点的网络.
结论:
- lFCI提供了一种有效的解决方案,用于稀疏的因果发现,高维设置与未测量的混因子和选择偏差.
- 该算法的处理枢纽节点的能力使其适合于现实世界的网络分析.
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