RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. (arXiv:2110.03031v3 [cs.LG] UPDATED)

Many causal and policy effects of interest are defined by linear functionals
of high-dimensional or non-parametric regression functions.
$\sqrt{n}$-consistent and asymptotically normal estimation of the object of
interest requires debiasing to reduce the effects of regularization and/or
model selection on the object of interest. Debiasing is typically achieved by
adding a correction term to the plug-in estimator of the functional, which
leads to properties such as semi-parametric efficiency, double robustness, and
Neyman orthogonality. We implement an automatic debiasing procedure based …

arxiv learning lg machine machine learning random random forests