Before revision:  We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners that can converge at relatively slow rates. We give conditions for root-n consistency and asymptotic normality of the functional learner. We give results for non affine functionals in addition to affine functionals .
After revision:  We give an asymptotic variance estimator for DML. We allow for a wide variety of regression learners and identifying equations are covered. Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income .