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Many causal and policy effects of interest are defined by linear functionalsof high-dimensional or non-parametric regression functions.$\sqrt{n}$-consistent and asymptotically normal estimation of the object ofinterest requires debiasing to reduce the effects of regularization and/ormodel selection on the object of interest. Debiasing is typically achieved byadding a correction term to the plug-in estimator of the functional, whichleads to properties such as semi-parametric efficiency, double robustness, andNeyman orthogonality. We implement an automatic debiasing procedure based onautomatically learning the Riesz representation of the linear functional usingNeural Nets and Random Forests. Our method only relies on black-box evaluationoracle access to the linear functional and does not require knowledge of itsanalytic form. We propose a multitasking Neural Net debiasing method withstochastic gradient descent minimization of a combined Riesz representer andregression loss, while sharing representation layers for the two functions. Wealso propose a Random Forest method which learns a locally linearrepresentation of the Riesz function. Even though our method applies toarbitrary functionals, we experimentally find that it performs well compared tothe state of art neural net based algorithm of Shi et al. (2019) for the caseof the average treatment effect functional. We also evaluate our method on theproblem of estimating average marginal effects with continuous treatments,using semi-synthetic data of gasoline price changes on gasoline demand.

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests