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Debiased machine learning based on regression requires estimation of an unknown debiasing function. We give an automatic method of estimating the debiasing function based on any machine learner. This method only depends on the parameter of interest and the machine learner. We give primitive regularity conditions for neural net estimation of the debiasing function and show its superior performance relative to inverse probability weighting in estimating the average treatment effect in a state of the art example.
