发表机构
Feedzai; Instituto Superior Técnico, Universidade de Lisboa; DCC, Faculdade de Ciêncidas da Universidade do Porto; Instituto de Telecomunicações（Feedzai公司; 里斯本大学高等技术学院; 波尔图大学理学院数据与计算机科学系; 电信研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在解决不规则时间序列的因果发现问题，通过扩展PCMCI+方法，利用预定义时间窗口聚合因果影响，在合成数据实验中表现出色，能有效恢复因果图且优于标准方法。
AI 中文摘要
因果发现方法在时间系统中表现出色，但通常依赖规则和离散滞后结构，限制了对不规则采样数据的适用性。本文提出扩展PCMCI+，一种用于规则多元时间序列因果发现的先进方法，以处理不规则时间序列。该方法通过预定义时间窗口聚合因果影响，而非固定滞后依赖建模。在不同信噪比下的合成不规则事件流上评估，结果表明它能一致地恢复潜在因果图，在不规则采样数据上显著优于标准PCMCI+。
英文摘要
Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions. In this work, we propose an extension of PCMCI+, a state-of-the-art method for causal discovery on regular multivariate time series, to allow for handling irregular time series. Instead of modelling causal relations through fixed-lag dependencies, our method aggregates causal influence over predefined temporal windows. We evaluate our method on synthetic irregular event streams with known causal structures under different signal-to-noise ratios, showing that it consistently recovers the underlying causal graph and substantially outperforms the standard PCMCI+ on irregularly sampled data.
