# 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" and Emmenegger and
Buehlmann (2021) "Double Machine Learning for Partially
Linear Mixed-Effects Models with Repeated Measurements".
First part: 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
confidence intervals.
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.
Second part: our goal is to estimate and perform inference for the linear
coefficient in a partially linear mixed-effects model
with DML. Machine learning algorithms allows us to incorporate more
complex interaction structures and high-dimensional variables.