Before revision:   The result is a best linear predictor for effect heterogeneity based on impact groups.
After revision:  To control for confounding in the linear model we use Neyman-orthogonal moments to partial out the effect that covariates have on both, the treatment assignment and the outcome. The result is a best linear predictor for effect heterogeneity based on impact groups.