One Thing Leads to Another: The Essentials of Time Series Causal Discovery

Key points

• This tutorial provides a unified overview of time series causal discovery across research communities.1
• We introduce the key principles invoked by modern discovery methods, and we discuss their limitations and assumptions.1
• We further discuss how causal discovery for time series is becoming crucial for downstream tasks in agriculture and Earth science, in healthcare, and in artificial intelligence systems.1
• We believe that this tutorial will serve as a valuable resource in navigating the deluge of recent causality research, as well as an invitation to signal processing audiences to contribute to causal problems.1

Sources (1)

• [1]One Thing Leads to Another: The Essentials of Time Series Causal Discovery
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 11:33 AM

  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.

  This tutorial provides a unified overview of time series causal discovery across research communities.

Extractive summary: sentences quoted from the sources.