Bayesian nonparametric models are a popular and effective tool for inferring the heterogeneous effects of interventions. I will discuss how to carefully specify models and prior distributions to apply judicious regularization of heterogeneous effects. I will also discuss how to extract answers to scientific and policy questions from a fitted nonparametric model using posterior summarization to avoid problems incurred by using competing or incompatible model specifications for targeting different estimands. Together these tools provide a general recipe for obtaining stable, generalizable and transferrable insights about heterogeneous effects.