Causal Counterfactual Theory for the Attribution of Weather and Climate-Related Events

By Alexis Hannart, Judea Pearl, Friederike E. L. Otto, Philippe Naveau, Michael Ghil. 2016

179 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science

View Paper: https://www.semanticscholar.org/paper/86e0f7eb8f762bbba5495f9a9c064df1960a8dec

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In Artificial Intelligence

In History and Philosophy of Science

Citations in the corpus

The physics of climate variability and climate change (2019, background): “At the same time, the attribution of unusual climatic conditions to specific climate forcing is far from being a trivial matter (Allen, 2003; Hannart et al., 2016a).”
Memory Matters: A Case for Granger Causality in Climate Variability Studies (2018, background): “…while formal deﬁnitions of causality exist as deﬁned by Pearl’s causal theory (Pearl 2009) and have been more recently introduced into climate science (e.g., Hannart et al. 2016), here, we loosely deﬁne a ‘‘causal relationship’’ as one that shows a signiﬁcant lagged relationship between variables.”
GENERATIVE ADVERSARIAL NETS (2018, methodology): “On IHDP, GANITE is competitive with BART and BNN but is outperformed by TARNET, CFRWASS and CMGP.”
The Book of Why: The New Science of Cause and Effect† (2019, background): “According to Pearl and a team of climate scientists, the response can be framed differently: There is a 90% chance that the 2003 heat wave in Europe would not have occurred in the absence of anthropogenic global warming (Hannart et al. 2016).”
Human contribution to the record-breaking June and July 2019 heatwaves in Western Europe (2020, background): “The observations give a trend, but do not allow to attribute the trend to a cause in the traditional Pearl interpretation (Pearl 1988, Hannart et al 2016).”
Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns (2015, background): “Note that the attribution of unusual climatic conditions to specific climate forcing is far from being a trivial matter [13,14].”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, methodology): “These range from variable selection for estimation and prediction tasks to identifying causal pathways of interactions around the globe, (see Figure 2), and causal attribution [93], [95].”
Spatio-Temporal Data Mining (2017, methodology): “The framework of Pearl causality has also been explored for identifying causal interactions in climate science (Ebert-Uphoff and Deng 2012, 2017; Hannart et al. 2016), where probabilistic graphical models were constructed using causal edges among the nodes.”