mlsanityData to 7 Oct 2026

Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods

arXiv 2404.05746 ↗First posted 3 Apr 2024physics.data-an cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 131 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

17 of 131 references.

References · 17

  1. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 154
  2. 2020CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learningcited by 109
  3. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  4. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  5. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
  6. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  7. 2013Causal Discovery from Changescited by 52
  8. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  9. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  10. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  11. 2020Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuescited by 18
  12. 2022Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Pastcited by 9
5 more
  1. 2023Spatial causal inference in the presence of unmeasured confounding and interferencecited by 9
  2. 2019Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Networkcited by 3
  3. 2023Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inferencecited by 3
  4. 2015Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulationscited by 1
  5. 2021A Primer on Deep Learning for Causal Inferencecited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025Spatio-Temporal Hierarchical Causal Modelscited by 2
  2. 2024Unified Causality Analysis Based on the Degrees of Freedomcited by 1