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.

翻译：因果发现问题利用一组观测数据来推断现实世界中变量间的因果关系，通常旨在回答有关生物或物理系统的问题。这些观测数据通常以固定的时间间隔记录，其间隔由用户或机器根据实验设计确定。但无法保证这些记录的时序与底层生物或物理事件的发生时序完全匹配。本文研究了因果发现方法对这种潜在失配的敏感性。我们通过实证与理论依据，探讨了采样率和窗口长度的变化如何影响因果发现的性能。研究表明，无论是经典方法还是近期提出的因果发现方法，均对这些超参数表现出敏感性；文中进一步讨论了如何借助信号处理的思想来理解这些现象。