A conditional independence graph is a concise representation of pairwise
conditional independence among many variables. We propose Graphical Random
Forests (GRaFo) for estimating pairwise conditional independence relationships
among mixed-type, i.e. continuous and discrete, variables. The number of edges
is a tuning parameter in any graphical model estimator and there is no obvious
number that constitutes a good choice. Stability Selection helps choosing this
parameter with respect to a bound on the expected number of false positives
(error control).