  Causal asymmetry is based on the principle that an event is a cause only if
its absence would not have been a cause. From there, uncovering causal effects
becomes a matter of comparing a well-defined score in both directions.
Motivated by studying causal effects at extreme levels of a multivariate random
vector, we propose to construct a model-agnostic causal score relying solely on
the assumption of the existence of a max-domain of attraction. Based on a
representation of a Generalized Pareto random vector, we construct the causal
score as the Wasserstein distance between the margins and a well-specified
random variable. The proposed methodology is illustrated on a hydrologically
simulated dataset of different characteristics of catchments in Switzerland:
discharge, precipitation, and snowmelt.
