  The linear coefficient in a partially linear model with confounding variables
can be estimated using double machine learning (DML). However, this DML
estimator has a two-stage least squares (TSLS) interpretation and may produce
overly wide confidence intervals. To address this issue, we propose a
regularization and selection scheme, regsDML, which leads to narrower
confidence intervals. It selects either the TSLS DML estimator or a
regularization-only estimator depending on whose estimated variance is smaller.
The regularization-only estimator is tailored to have a low mean squared error.
The regsDML estimator is fully data driven. The regsDML estimator converges at
the parametric rate, is asymptotically Gaussian distributed, and asymptotically
equivalent to the TSLS DML estimator, but regsDML exhibits substantially better
finite sample properties. The regsDML estimator uses the idea of k-class
estimators, and we show how DML and k-class estimation can be combined to
estimate the linear coefficient in a partially linear endogenous model.
Empirical examples demonstrate our methodological and theoretical developments.
Software code for our regsDML method is available in the R-package dmlalg.
