Causal Discovery For Climate

I am very happy to see how Machine Learning is contributing to climate prediction. In particular, the initiative Causality 4 Climate tries to establish the maturity some models may have to infer causes leading to extreme climate processes happening nowadys.

The following paper is particularly in order:

Jakob Runge, Sebastian Bathiany, Erik Bollt, Gustau Camps-Valls, Dim Coumou, Ethan Deyle, Clark Glymour, Marlene Kretschmer, Miguel D. Mahecha, Jordi Muñoz-Marí, Egbert H. van Nes, Jonas Peters, Rick Quax, Markus Reichstein, Marten Scheffer, Bernhard Schölkopf, Peter Spirtes, George Sugihara, Jie Sun, Kun Zhang & Jakob Zscheischler, 
"Inferring causation from time series in Earth system sciences"
Nature Communications volume 10, Article number: 2553 (2019)

Link to www of the paper, HERE.

I am also proud to know some of the authors :-)

Try not to learn my [confounding] biases!

Dr. David Lopez-Paz's research never deceives. His (I think) last published work goes to the main core of the classifier training process, i. e., we should try to somehow avoid classifiers to learn spurious (not consistent) relationships that establish on the training data and which make generalization ability difficult to carry out. Their aim is to develop "invariant" and "causal predictors", to enable a "good generalisation behaviour".

Important (well, at least I consider so) piece of research:

Martin Arjovsky, Léon Bottou, Ishaan Gulrajani and David Lopez-Paz, "Invariant Risk Minimization", arXiv:1907.02893, 2019.

https://arxiv.org/abs/1907.02893

Link to pdf file HERE.