This document proposes pretraining strategies for estimating heterogeneous treatment effects (HTE), particularly the conditional average treatment effect (CATE), which varies based on individual characteristics. The core idea is to leverage the shared support between factors that are prognostic of the outcome (influence baseline risk) and those that are predictive of treatment effect heterogeneity (interact with treatment). The authors demonstrate this approach primarily within the R-learner framework, showing that using information from the prediction of the mean outcome can improve the accuracy, power, and support recovery for estimating the CATE, especially in challenging, high-dimensional settings where data may be limited. They also discuss extending this concept to the DR-learner and non-linear models like generalized random forests, suggesting it could be beneficial when such overlap between prognostic and predictive factors exists.