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.


Reference manual

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0.1.1 by Malte S. Kurz, a month ago

Browse source code at

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