Double Machine Learning in R

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. 'DoubleML' allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. 'DoubleML' is built on top of 'mlr3' and the 'mlr3' ecosystem. The object-oriented implementation of 'DoubleML' based on the 'R6' package is very flexible.


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install.packages("DoubleML")

0.1.1 by Malte S. Kurz, a month ago


https://github.com/DoubleML/doubleml-for-r/


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


Authors: Philipp Bach [aut] , Victor Chernozhukov [aut] , Malte S. Kurz [aut, cre] , Martin Spindler [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports R6, data.table, stats, checkmate, mlr3, mlr3tuning, mvtnorm, utils, clusterGeneration, readstata13

Suggests knitr, rmarkdown, testthat, patrick, mlr3learners, paradox, dplyr, glmnet, lgr, ranger, sandwich, AER, rpart


See at CRAN