  Heterogeneous treatment effects can be very important in the analysis of
randomized clinical trials. Heightened risks or enhanced benefits may exist for
particular subsets of study subjects. When the heterogeneous treatment effects
are specified as the research is being designed, there are proper and readily
available analysis techniques. When the heterogeneous treatment effects are
inductively obtained as an experiment's data are analyzed, significant
complications are introduced. There can be a need for special loss functions
designed to find local average treatment effects and for techniques that
properly address post selection statistical inference. In this paper, we tackle
both while undertaking a recursive partitioning analysis of a randomized
clinical trial testing whether individuals on probation, who are low risk, can
be minimally supervised with no increase in recidivism.
