  A variety of interesting parameters may depend on high dimensional
regressions. Machine learning can be used to estimate such parameters. However
estimators based on machine learners can be severely biased by regularization
and/or model selection. Debiased machine learning uses Neyman orthogonal
estimating equations to reduce such biases. Debiased machine learning generally
requires estimation of unknown Riesz representers. A primary innovation of this
paper is to provide Riesz regression estimators of Riesz representers that
depend on the parameter of interest, rather than explicit formulae, and that
can employ any machine learner, including neural nets and random forests.
End-to-end algorithms emerge where the researcher chooses the parameter of
interest and the machine learner and the debiasing follows automatically.
Another innovation here is debiased machine learners of parameters depending on
generalized regressions, including high-dimensional generalized linear models.
An empirical example of automatic debiased machine learning using neural nets
is given. We find in Monte Carlo examples that automatic debiasing sometimes
performs better than debiasing via inverse propensity scores and never worse.
Finite sample mean square error bounds for Riesz regression estimators and
asymptotic theory are also given.
