Models in
software engineering
allow developers to define, but not necessarily
explore,
the space of design options.
Building a useful model,
understanding all its interactions,
can be intellectually difficult- particularly for
large non-linear discrete models.
We show theoretically and empirically that
our TAR2 {\em minimal contrast set} learner can generate
very succinct conclusions from seemingly complex spaces.
Treatment learning is indicated
for high-dimensionality
non-linear, non-continuous models built in domains with noise or other
uncertainties; where managers do not have full control over all model inputs;
where there is a requirement to explain or audit models; 
when reducing model variance is
valuable, and there does not exist a budget for building and using
maximal models.
