  Causal inference for extremes aims to discover cause and effect relations
between large observed values of random variables. Over the last years, a
number of methods have been proposed for solving the Hidden River Problem, with
the Danube data set as benchmark. In this paper, we provide \QTree, a new and
simple algorithm to solve the Hidden River Problem that outperforms existing
methods. \QTree\ returns a directed graph and achieves almost perfect recovery
on the Danube as well as on new data from the Lower Colorado River. It can
handle missing data, has an automated parameter tuning procedure, and runs in
time $O(n |V|^2)$, where $n$ is the number of observations and $|V|$ the number
of nodes in the graph. \QTree\ relies on qualitative aspects of the max-linear
Bayesian network model.
