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" . 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.


Reference manual

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0.0.2 by Corinne Emmenegger, 2 days ago

Browse source code at

Authors: Corinne Emmenegger [aut, cre] , Peter Buehlmann [ths]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports glmnet, matrixcalc, stats, splines, randomForest

Suggests testthat

See at CRAN