提出新方法从事件序列中识别隐藏因果结构，解决部分观测难题。
Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
- 将连续事件转为离散因果模型，基于路径条件推断隐藏过程
- 在合成与真实数据上均有效恢复因果结构，即使存在未观测过程
- 适合研究复杂系统中隐藏因果关系的学者，如金融、神经科学领域
多变量霍克斯过程为建模复杂系统中的时序依赖与事件驱动交互提供了有力框架。现有方法主要关注可观测子过程间的因果结构，但现实系统常仅部分可观测，隐藏子过程带来显著挑战。本文表明，当时间间隔趋近于零时，连续时间事件序列可表示为离散时间因果模型，并据此建立识别隐藏子过程及因果影响的必要充分条件。为此，我们提出两阶段迭代算法，交替进行子过程间因果关系推断与新隐藏子过程发现，由保证可识别性的路径条件引导。在合成与真实数据集上的实验表明，该方法在存在隐藏子过程的情况下仍能有效恢复因果结构。
原文摘要 · Abstract (English)
Multivariate Hawkes process provides a powerful framework for modeling temporal dependencies and event-driven interactions in complex systems. While existing methods primarily focus on uncovering causal structures among observed subprocesses, real-world systems are often only partially observed, with latent subprocesses posing significant challenges. In this paper, we show that continuous-time event sequences can be represented by a discrete-time causal model as the time interval shrinks, and we leverage this insight to establish necessary and sufficient conditions for identifying latent subprocesses and the causal influences. Accordingly, we propose a two-phase iterative algorithm that alternates between inferring causal relationships among discovered subprocesses and uncovering new latent subprocesses, guided by path-based conditions that guarantee identifiability. Experiments on both synthetic and real-world datasets show that our method effectively recovers causal structures despite the presence of latent subprocesses.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。