Double Machine Learning Algorithms
Implementation of double machine learning (DML) algorithms in R,
based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning
in Partially Linear Endogenous Models" <2101.12525>.
Our goal is to perform inference for the linear parameter in partially
linear models with confounding variables.
The standard DML estimator of the linear parameter has a two-stage least
squares interpretation, which can lead to a large variance and overwide
We apply regularization to reduce the variance of the estimator,
which produces narrower confidence intervals that are approximately valid.
Nuisance terms can be flexibly estimated with machine learning algorithms.2101.12525>