Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the use of a known potential confounder as a covariate and allows its effect to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset.

翻译：在很多情况下,复杂的因果机制只表现在极端事件中,或采取更简单的极端形式。根据关于极端河流流量和降水的数据,我们为重尾变数引入了新的因果发现方法,允许使用已知的潜在混淆者作为共变体,并在变量有可比尾巴时几乎完全消除其影响,并且还足够减少其影响,以便能够在构造者尾巴变重时进行正确的因果推断。我们为现有的因果尾系数和变异测试引入了新的参数估测器。模拟显示,这些方法运作良好,其想法被运用到激励数据集中。