  Estimating heterogeneous treatment effects is a well-studied topic in the
statistics literature. More recently, it has regained attention due to an
increasing need for precision medicine as well as the increased use of
state-of-art machine learning methods in the estimation. Furthermore,
estimating heterogeneous treatment effects is directly related to building an
individualized treatment rule, which is a decision rule of treatment according
to patient characteristics. This paper examines the connection and
disconnection between these two research problems. Notably, a better estimation
of the heterogeneous treatment effects may or may not lead to a better
individualized treatment rule. We provide theoretical frameworks to explain the
connection and disconnection and demonstrate two different scenarios through
simulations. Our conclusion sheds light on a practical guide that under certain
circumstances, there is no need to enhance estimation of the treatment effects,
as it does not alter the treatment decision.
