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三重线下因果发现基于最佳马尔科夫毯及其应用
Waqar Khan1, Brekhna Brekhna2, Jianqiong Huang3
1School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China. wangkang@fzfu.edu.cn.
Scientific reports
|November 18, 2025
概括
本研究介绍了T-OCDmb,这是一种新的因果特征选择框架,可以提高预测准确性和计算效率,用于从观测数据中识别因果关系.
科学领域:
- 因果推理的原因推理.
- 机器学习是机器学习.
- 统计建模 统计建模
背景情况:
- 基于线下约束的因果特征选择 (OC-CFS) 对于发现观测数据中的因果关系至关重要.
- 现有的OC-CFS方法在预测准确性和计算成本方面面临挑战,特别是在不同的样本大小中.
研究的目的:
- 开发一个新的框架,T-OCDmb,通过提高预测准确性和减少计算开销来增强因果特征选择.
- 解决现有的OC-CFS算法的局限性,特别是与不同样本大小的性能有关.
主要方法:
- T-OCDmb集成了HITON-MB父母和儿童 (PC) 战略,用于相关节点识别.
- 它采用BAMB策略来检测相关的配偶和STMB非马科夫毯 (非MB) 策略来排除非MB后代.
- 该框架旨在通过结合这些策略来准确识别马尔科夫毯 (MB).
主要成果:
- 在基准贝叶斯网络和现实世界数据集上,T-OCDmb在预测准确度和计算效率方面取得了显著的改进.
- 在小样本大小 (n=500) 上,T-OCDmb在5/7数据集中实现了最高的回忆 (超过20%的改善).
- 在大样本大小 (n=5000) 上,T-OCDmb在4/7数据集中达到最高精度 (平均94%) 并具有计算效率,比平均竞争对手快35%.
结论:
- T-OCDmb有效地识别了真正的马尔科夫毯,准确度高,运行时间缩短,性能优于现有方法.
- 该框架为因果特征选择提供了一个强大的解决方案,特别有利于具有不同样本大小的数据集.
- 源代码是公开可用的,用于进一步的研究和应用.
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