Nature Communications· 2019Q1
Inferring causation from time series in Earth system sciences
- 962citations
- Q1SCImago
- 2019year
Short summary
A new benchmark platform, causeme.net, is launched to facilitate data-driven causal inference from time series data in complex systems like Earth science, bridging the gap between method developers and users.
AI-generated from the title and abstract; the full text is not read.
Key points
- Real experiments are rarely feasible in large-scale complex dynamical systems like the Earth system.
- Increasing observational and simulated data enables data-driven causal inference methods beyond simple correlation.
- The paper reviews causal inference frameworks and identifies common application cases in Earth system sciences.
- The benchmark platform causeme.net is launched to connect method users and developers.
AI-generated from the title and abstract; the full text is not read.
Abstract
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
The authors' abstract, as published at the source. Nature Communications, 2019 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science