Many experiments on a system or process have multiple responses of interest, each with potentially different data types and hence underlying distributions. However, traditionally, designing the ...

We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree ...