  The past years have seen seen the development and deployment of
machine-learning algorithms to estimate personalized treatment-assignment
policies from randomized controlled trials. Yet such algorithms for the
assignment of treatment typically optimize expected outcomes without taking
into account that treatment assignments are frequently subject to hypothesis
testing. In this article, we explicitly take significance testing of the effect
of treatment-assignment policies into account, and consider assignments that
optimize the probability of finding a subset of individuals with a
statistically significant positive treatment effect. We provide an efficient
implementation using decision trees, and demonstrate its gain over selecting
subsets based on positive (estimated) treatment effects. Compared to standard
tree-based regression and classification tools, this approach tends to yield
substantially higher power in detecting subgroups with positive treatment
effects.
