# Rethinking Chronological Causal Discovery with Signal Processing

arXiv: https://arxiv.org/abs/2602.19903 · HTML: https://mlsanity.com/paper/2602.19903/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [Approximate Causal Abstraction](https://mlsanity.com/paper/1906.11583/) (2019)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Causal Sufficiency and Actual Causation](https://mlsanity.com/paper/2102.02311/) (2021)
- [Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice](https://mlsanity.com/paper/2310.18212/) (2023)
- [Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems](https://mlsanity.com/paper/2410.23499/) (2024)
- [A Generalized Nyquist-Shannon Sampling Theorem Using the Koopman Operator](https://mlsanity.com/paper/2303.01927/) (2023)
- [Causal Ordering for Structure Learning from Time Series](https://mlsanity.com/paper/2510.24639/) (2025)

## Built on by (most cited first, 1 of 1)

- [One Thing Leads to Another: The Essentials of Time Series Causal Discovery](https://mlsanity.com/paper/2610.13430/) (2026)

## Abstract (arXiv)

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.