Extreme hydrological events in the Danube river basin may severely impact human populations, aquatic organisms and economic activity. One often characterizes the joint structure of extreme events by using the theory of multivariate and spatial extremes and its asymptotically justified models. There is interest, however, in cascading extreme events and whether one event causes another. We argue that an improved understanding of the mechanism underlying severe events is achieved by combining extreme value modelling and causal discovery. We construct a causal inference method relying on the notion of the Kolmogorov complexity of extreme conditional quantiles. Tail quantities are derived by using multivariate extreme value models, and causal‐induced asymmetries in the data are explored through the minimum description length principle. Our method CausEV for causality for extreme values uncovers causal relationships between summer extreme river discharges in the upper Danube basin and finds significant causal links between the Danube and its Alpine tributary Lech.

中文翻译：

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多瑙河上游流域网中极端河流排放的成因机理

多瑙河流域的极端水文事件可能会严重影响人口，水生生物和经济活动。人们经常使用多元和空间极端理论及其渐近合理模型来刻画极端事件的联合结构。但是，人们关注级联极端事件以及一个事件是否引起另一事件的兴趣。我们认为通过结合极端价值建模和因果发现，可以更好地理解严重事件的发生机理。我们基于极端条件分位数的Kolmogorov复杂度的概念构造因果推理方法。通过使用多元极值模型导出尾量，并通过最小描述长度原理探索数据中因果关系引起的不对称性。