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[2602.19903] Rethinking Chronological Causal Discovery with Signal Processing
Summary
This paper explores the impact of sampling rates and window lengths on causal discovery methods, highlighting the sensitivity of these methods to hyperparameters and suggesting that signal processing techniques may provide insights into these challenges.
Why It Matters
Understanding the sensitivity of causal discovery methods to sampling parameters is crucial for researchers in fields like biology and physics, where accurate causality inference can significantly impact experimental outcomes. This paper bridges the gap between signal processing and causal inference, offering new perspectives on improving methodologies.
Key Takeaways
- Causal discovery methods are sensitive to changes in sampling rate and window length.
- Both classical and modern methods exhibit performance variations based on hyperparameters.
- Signal processing concepts can enhance the understanding of causal discovery challenges.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.19903 (eess) [Submitted on 23 Feb 2026] Title:Rethinking Chronological Causal Discovery with Signal Processing Authors:Kurt Butler, Damian Machlanski, Panagiotis Dimitrakopoulos, Sotirios A. Tsaftaris View a PDF of the paper titled Rethinking Chronological Causal Discovery with Signal Processing, by Kurt Butler and 3 other authors View PDF HTML (experimental) Abstract: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. Comments: Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG); Machine Learning (stat.ML) MSC classes: 62M10 Cite as: arXiv:2602.19903 ...