发现采样率和窗口长度会显著影响因果发现效果，需用信号处理思想改进。
Rethinking Chronological Causal Discovery with Signal Processing
- 分析采样率与窗口长度对因果推断的影响机制
- 实证显示经典与新方法均受采样参数敏感性干扰
- 建议引入信号处理理论提升因果发现鲁棒性
因果发现旨在通过观测数据推断变量间的因果关系，常用于生物或物理系统分析。这些观测通常以固定时间间隔记录，由实验设计决定。然而，记录时间未必与真实事件发生时间同步。本文研究因果发现方法对这种时间错配的敏感性，结合实证与理论证据，探讨采样率和窗口长度变化如何影响性能。结果表明，经典与新兴因果发现方法均对这些超参数敏感，且信号处理的思想有助于理解此类现象。
原文摘要 · Abstract (English)
Causal discovery problems use a set of observations to deduce causality between variables in the real world, typically to answer questions about biological or physical systems. These observations are often recorded at regular time intervals, determined by a user or a machine, depending on the experiment design. There is generally no guarantee that the timing of these recordings matches the timing of the underlying biological or physical events. In this paper, we examine the sensitivity of causal discovery methods to this potential mismatch. We consider empirical and theoretical evidence to understand how causal discovery performance is impacted by changes of sampling rate and window length. We demonstrate that both classical and recent causal discovery methods exhibit sensitivity to these hyperparameters, and we discuss how ideas from signal processing may help us understand these phenomena.
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