Rethinking Chronological Causal Discovery with Signal Processing

TL;DR

This paper investigates how the accuracy of causal discovery methods is affected by sampling rate and window length mismatches, using empirical, theoretical, and signal processing insights to improve understanding.

Contribution

It provides a comprehensive analysis of the sensitivity of causal discovery to sampling parameters and proposes signal processing perspectives to address these challenges.

Findings

Classical and recent causal discovery methods are sensitive to sampling rate and window length.

Signal processing ideas can help understand and mitigate these sensitivities.

Performance varies with changes in sampling parameters, affecting causal inference accuracy.

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.