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

arXiv: https://arxiv.org/abs/2404.05746 · HTML: https://mlsanity.com/paper/2404.05746/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 131 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations](https://mlsanity.com/paper/2002.04083/) (2020)
- [CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning](https://mlsanity.com/paper/2010.04296/) (2020)
- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [High-recall causal discovery for autocorrelated time series with latent confounders](https://mlsanity.com/paper/2007.01884/) (2020)
- [CausalML: Python Package for Causal Machine Learning](https://mlsanity.com/paper/2002.11631/) (2020)
- [Causal Discovery from Changes](https://mlsanity.com/paper/1301.2312/) (2013)
- [Causal Discovery Toolbox: Uncover causal relationships in Python](https://mlsanity.com/paper/1903.02278/) (2019)
- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values](https://mlsanity.com/paper/2002.09573/) (2020)
- [Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past](https://mlsanity.com/paper/2203.06056/) (2022)
- [Spatial causal inference in the presence of unmeasured confounding and interference](https://mlsanity.com/paper/2303.08218/) (2023)
- [Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Network](https://mlsanity.com/paper/1909.03704/) (2019)
- [Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference](https://mlsanity.com/paper/2303.07122/) (2023)
- [Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations](https://mlsanity.com/paper/1512.08279/) (2015)
- [A Primer on Deep Learning for Causal Inference](https://mlsanity.com/paper/2110.04442/) (2021)

## Built on by (most cited first, 2 of 2)

- [Spatio-Temporal Hierarchical Causal Models](https://mlsanity.com/paper/2511.20558/) (2025)
- [Unified Causality Analysis Based on the Degrees of Freedom](https://mlsanity.com/paper/2410.19469/) (2024)

## 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.