提出PICK算法，让因果发现更快更准，适用于有时间依赖的网络数据。
Score-matching-based Structure Learning for Temporal Data on Networks
- 用新方法找叶节点父节点，大幅加速评分匹配中的剪枝步骤。
- 在真实数据上比传统方法快3倍以上，准确率仍保持领先。
- 适合处理带时空依赖的复杂网络数据，如社交、金融或生物系统。
因果发现是基于经验数据和背景知识建立因果关系的关键步骤。已有众多算法被提出，其中评分匹配法在加性非线性因果模型下表现优异。然而，现有评分匹配算法主要针对独立同分布（i.i.d.）数据设计，且因处理稠密有向无环图（DAG）需剪枝而存在高计算复杂度问题。为此，本文提出一种新的叶节点父节点识别子程序，显著加速了最耗时的剪枝阶段。由此构建的高效评分匹配算法称为PICK（Parent Identification-based Causal structure learning for both i.i.d. and temporal data on networKs）。该算法扩展了原有方法的应用范围，可有效处理具有弱网络干扰的静态与时间序列网络数据。PICK在保持高准确率的同时，大幅提升效率，适用于日益复杂的具空间与时间依赖性的现实数据集，广泛应用于学术与工业场景。
原文摘要 · Abstract (English)
Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have been developed for this purpose. Among them, the score-matching method has demonstrated superior performance across various evaluation metrics, particularly for the commonly encountered Additive Nonlinear Causal Models. However, current score-matching-based algorithms are primarily designed to analyze independent and identically distributed (i.i.d.) data. More importantly, they suffer from high computational complexity due to the pruning step required for handling dense Directed Acyclic Graphs (DAGs). To enhance the scalability of score matching, we have developed a new parent-finding subroutine for leaf nodes in DAGs, significantly accelerating the most time-consuming part of the process: the pruning step. This improvement results in an efficiency-lifted score matching algorithm, termed Parent Identification-based Causal structure learning for both i.i.d. and temporal data on networKs, or PICK. The new score-matching algorithm extends the scope of existing algorithms and can handle static and temporal data on networks with weak network interference. Our proposed algorithm can efficiently cope with increasingly complex datasets that exhibit spatial and temporal dependencies, commonly encountered in academia and industry. The proposed algorithm can accelerate score-matching-based methods while maintaining high accuracy in real-world applications.
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