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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dmlalg")

0.0.2 by Corinne Emmenegger, 2 days ago


https://gitlab.math.ethz.ch/ecorinne/dmlalg.git


Browse source code at https://github.com/cran/dmlalg


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