English(EN) One Thing Leads to Another: The Essentials of Time Series Causal Discovery

时间序列因果发现：跨越人工智能与科学的统一概览

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

本文对时间序列因果发现这一领域进行了统一的概览。该领域起源于信号处理，现已扩展到统计学、动力系统和机器学习。文章概述了现代发现方法的核心原则、局限性和假设，以及评估技术。研究强调了时间序列因果发现对于农业、地球科学、医疗保健和人工智能系统等应用日益增长的重要性，旨在为因果关系研究提供资源。 AI

影响为在人工智能系统中应用因果发现技术的研究人员和实践者提供了基础性概览。

排序理由该条目是一篇在arXiv上发表的学术论文，详细介绍了某一特定领域的研究。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 24 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目是一篇在arXiv上发表的学术论文，详细介绍了某一特定领域的研究。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv stat.ML TIER_1 English(EN) · Kurt Butler, Damian Machlanski, Michael P. J. Camilleri, Zhihua Liu, Panagiotis Dimitrakopoulos, Feng Chen, Ricardo Silva, Sotirios A. Tsaftaris · 2026-10-12 04:00

一因生一果：时间序列因果发现要点

arXiv:2610.13430v1 Announce Type: cross Abstract: Time series causal discovery --- the process of detecting causation between time series --- has its roots in signal processing research from over a half century ago. It has since received active and widespread interest, evolving i…