Causality for Earth Science -- A Review on Time-series and
  Spatiotemporal Causality Methods
Sahara Ali Uzma Hasan Xingyan Li Omar Faruque Akila Sampath Yiyi Huang Md Osman Gani Jianwu Wang
This survey paper covers the breadth and depth of time-series and
spatiotemporal causality methods, and their applications in Earth Science. More
specifically, the paper presents an overview of causal discovery and causal
inference, explains the underlying causal assumptions, and enlists evaluation
techniques and key terminologies of the domain area. The paper elicits the
various state-of-the-art methods introduced for time-series and spatiotemporal
causal analysis along with their strengths and limitations. The paper further
describes the existing applications of several methods for answering specific
Earth Science questions such as extreme weather events, sea level rise,
teleconnections etc. This survey paper can serve as a primer for Data Science
researchers interested in data-driven causal study as we share a list of
resources, such as Earth Science datasets (synthetic, simulated and
observational data) and open source tools for causal analysis. It will equally
benefit the Earth Science community interested in taking an AI-driven approach
to study the causality of different dynamic and thermodynamic processes as we
present the open challenges and opportunities in performing causality-based
Earth Science study.