  Subgroup analysis of treatment effects plays an important role in
applications from medicine to public policy to recommender systems. It allows
physicians (for example) to identify groups of patients for whom a given drug
or treatment is likely to be effective and groups of patients for which it is
not. Most of the current methods of subgroup analysis begin with a particular
algorithm for estimating individualized treatment effects (ITE) and identify
subgroups by maximizing the difference across subgroups of the average
treatment effect in each subgroup. These approaches have several weaknesses:
they rely on a particular algorithm for estimating ITE, they ignore
(in)homogeneity within identified subgroups, and they do not produce good
confidence estimates. This paper develops a new method for subgroup analysis,
R2P, that addresses all these weaknesses. R2P uses an arbitrary, exogenously
prescribed algorithm for estimating ITE and quantifies the uncertainty of the
ITE estimation, using a construction that is more robust than other methods.
Experiments using synthetic and semi-synthetic datasets (based on real data)
demonstrate that R2P constructs partitions that are simultaneously more
homogeneous within groups and more heterogeneous across groups than the
partitions produced by other methods. Moreover, because R2P can employ any ITE
estimator, it also produces much narrower confidence intervals with a
prescribed coverage guarantee than other methods.
