The Causality for Climate Competition

Jakob Runge, Xavier-Andoni Tibau, Matthias Bruhns, Jordi Muñoz-Marí, Gustau Camps-Valls
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:110-120, 2020.

Abstract

Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being climate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is that current methods are not well adapted to climate data challenges. We here present an overview of a NeurIPS 2019 competition on causal discovery for climate time series. The Causality 4 Climate (C4C) competition was hosted on the benchmark platform {www.causeme.net}. C4C offers an extensive number of climate model-based time series datasets with known causal ground truth that incorporate the main challenges of causal discovery in climate research. We give an overview over the benchmark platform, the challenges modeled, how datasets were generated, and implementation details. The goal of C4C is to spur more focused methodological research on causal discovery for understanding our climate system.

Cite this Paper

BibTeX
@InProceedings{pmlr-v123-runge20a, title = {The Causality for Climate Competition}, author = {Runge, Jakob and Tibau, Xavier-Andoni and Bruhns, Matthias and Mu\~{n}oz-Mar\'{i}, Jordi and Camps-Valls, Gustau}, booktitle = {Proceedings of the NeurIPS 2019 Competition and Demonstration Track}, pages = {110--120}, year = {2020}, editor = {Escalante, Hugo Jair and Hadsell, Raia}, volume = {123}, series = {Proceedings of Machine Learning Research}, month = {08--14 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v123/runge20a/runge20a.pdf}, url = {https://proceedings.mlr.press/v123/runge20a.html}, abstract = {Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being climate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is that current methods are not well adapted to climate data challenges. We here present an overview of a NeurIPS 2019 competition on causal discovery for climate time series. The Causality 4 Climate (C4C) competition was hosted on the benchmark platform {www.causeme.net}. C4C offers an extensive number of climate model-based time series datasets with known causal ground truth that incorporate the main challenges of causal discovery in climate research. We give an overview over the benchmark platform, the challenges modeled, how datasets were generated, and implementation details. The goal of C4C is to spur more focused methodological research on causal discovery for understanding our climate system.} }
Endnote
%0 Conference Paper %T The Causality for Climate Competition %A Jakob Runge %A Xavier-Andoni Tibau %A Matthias Bruhns %A Jordi Muñoz-Marí %A Gustau Camps-Valls %B Proceedings of the NeurIPS 2019 Competition and Demonstration Track %C Proceedings of Machine Learning Research %D 2020 %E Hugo Jair Escalante %E Raia Hadsell %F pmlr-v123-runge20a %I PMLR %P 110--120 %U https://proceedings.mlr.press/v123/runge20a.html %V 123 %X Understanding the complex interdependencies of processes in our climate system has become one of the most critical challenges for society with our main current tools being climate modeling and observational data analysis, in particular observational causal discovery. Causal discovery is still in its infancy in Earth sciences and a major issue is that current methods are not well adapted to climate data challenges. We here present an overview of a NeurIPS 2019 competition on causal discovery for climate time series. The Causality 4 Climate (C4C) competition was hosted on the benchmark platform {www.causeme.net}. C4C offers an extensive number of climate model-based time series datasets with known causal ground truth that incorporate the main challenges of causal discovery in climate research. We give an overview over the benchmark platform, the challenges modeled, how datasets were generated, and implementation details. The goal of C4C is to spur more focused methodological research on causal discovery for understanding our climate system.
APA
Runge, J., Tibau, X., Bruhns, M., Muñoz-Marí, J. & Camps-Valls, G.. (2020). The Causality for Climate Competition. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, in Proceedings of Machine Learning Research 123:110-120 Available from https://proceedings.mlr.press/v123/runge20a.html.

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