[2412.07469] Score-matching-based Structure Learning for Temporal Data on Networks

Statistics > Machine Learning arXiv:2412.07469 (stat) [Submitted on 10 Dec 2024 (v1), last revised 6 Apr 2026 (this version, v2)] Title:Score-matching-based Structure Learning for Temporal Data on Networks Authors:Hao Chen, Kai Yi, Yu Guang Wang View a PDF of the paper titled Score-matching-based Structure Learning for Temporal Data on Networks, by Hao Chen and 2 other authors View PDF HTML (experimental) Abstract: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 algorit...