In this talk, we introduce a statistical framework for optimal treatment selection for a subgroup of patients, using their biomarker values based on casual inference. This new method is based on conditional quantile treatment effect (CQTE) curve, and CQTE curve’s simultaneous confidence bands (SCBs), which can be used to represent the quantile treatment effect for a given value of the biomarker and select an optimal treatment for one particular patient. We then propose B-splines methods for estimating the CQTE curves and constructing simultaneous confidence bands for the CQTE curves. We derive the asymptotic properties of the proposed methods. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed simultaneous confidence bands. Finally, we illustrate the application of the CQTE curve and its simultaneous confidence bands in optimal treatment selection in a real-world data set. This is a joint work with Dr. Kaishan Han.