Randomized experiments have become ubiquitous in many fields. Traditionally, we have focused on reporting the average treatment effect (ATE) from such experiments. With recent advances in machine learning, and the overall scale at which experiments are now conducted, we can broaden our analysis to include heterogeneous treatment effects. This provides a more nuanced view of the effect of a treatment or change on the outcome of interest. Going one step further, we can use models of heterogeneous treatment effects to optimally allocate treatment.In this talk will provide a brief overview of heterogeneous treatment effect modeling. We will show how to apply some recently proposed methods using R, and compare the results of each using a question wording experiment from the General Social Survey. Finally, we will conclude with some practical issues in modeling heterogeneous treatment effects, including model selection and obtaining valid confidence intervals.