  We present nonparametric algorithms for estimating optimal individualized
treatment rules. The proposed algorithms are based on the XGBoost algorithm,
which is known as one of the most powerful algorithms in the machine learning
literature. Our main idea is to model the conditional mean of clinical outcome
or the decision rule via additive regression trees, and use the boosting
technique to estimate each single tree iteratively. Our approaches overcome the
challenge of correct model specification, which is required in current
parametric methods. The major contribution of our proposed algorithms is
providing efficient and accurate estimation of the highly nonlinear and complex
optimal individualized treatment rules that often arise in practice. Finally,
we illustrate the superior performance of our algorithms by extensive
simulation studies and conclude with an application to the real data from a
diabetes Phase III trial.
