用整数规划高效发现贝叶斯网络的马尔可夫等价类
MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming
- 基于团聚焦策略与扩展最大支撑图，优化搜索路径
- 计算时间显著减少，因果发现准确率在多数据集提升
- 适合需要高效因果推断的科研与工程人员
本文提出一种新型整数规划（IP）方法，通过观测数据高效发现贝叶斯网络（BNs）的马尔可夫等价类（MEC）。MEC-IP算法采用独特的团聚焦策略和扩展最大支撑图（EMSG），有效简化MEC搜索过程，克服了现有算法固有的计算瓶颈。实验结果表明，该算法不仅大幅降低计算耗时，还在多种数据集上提升了因果发现的准确性。这些成果表明，该算法在因果推断与贝叶斯网络结构学习领域具有重要应用潜力，为复杂数据结构的高效精准分析提供了有力工具。
原文摘要 · Abstract (English)
This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maximal Spanning Graphs (EMSG) to streamline the search for MEC, thus overcoming the computational limitations inherent in other existing algorithms. Our numerical results show that not only a remarkable reduction in computational time is achieved by our algorithm but also an improvement in causal discovery accuracy is seen across diverse datasets. These findings underscore this new algorithm's potential as a powerful tool for researchers and practitioners in causal discovery and BNSL, offering a significant leap forward toward the efficient and accurate analysis of complex data structures.
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