One Thing Leads to Another: The Essentials of Time Series Causal Discovery
Time series causal discovery --- the process of detecting causation between time series --- has its roots in signal processing research from over a half century ago. It has since received active and widespread interest, evolving into different forms across domains such as statistics, dynamical systems and machine learning. Entering this fragmented and constantly evolving research landscape can be daunting. This tutorial provides a unified overview of time series causal discovery across research communities. We introduce the key principles invoked by modern discovery methods, and we discuss their limitations and assumptions. We also discuss how discovery methods are evaluated, benchmarked ...
| Authors | Kurt Butler, Damian Machlanski, Michael P. J. Camilleri et al. |
|---|---|
| Primary category | eess.SP |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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