  Researchers often run resource-intensive randomized controlled trials (RCTs)
to estimate the causal effects of interventions on outcomes of interest. Yet
these outcomes are often noisy, and estimated overall effects can be small or
imprecise. Nevertheless, we may still be able to produce reliable evidence of
the efficacy of an intervention by finding subgroups with significant effects.
In this paper, we propose a machine-learning method that is specifically
optimized for finding such subgroups in noisy data. Unlike available methods
for personalized treatment assignment, our tool is fundamentally designed to
take significance testing into account: it produces a subgroup that is chosen
to maximize the probability of obtaining a statistically significant positive
treatment effect. We provide a computationally efficient implementation using
decision trees and demonstrate its gain over selecting subgroups based on
positive (estimated) treatment effects. Compared to standard tree-based
regression and classification tools, this approach tends to yield higher power
in detecting subgroups affected by the treatment.
