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.