ddml: Double/Debiased Machine Learning

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base learners to increase robustness to the underlying data generating process.

Version: 0.2.2
Depends: R (≥ 3.6)
Imports: methods, stats, AER, MASS, Matrix, nnls, quadprog, glmnet, ranger, xgboost
Suggests: sandwich, covr, testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2024-06-27
DOI: 10.32614/CRAN.package.ddml
Author: Achim Ahrens [aut], Christian B Hansen [aut], Mark E Schaffer [aut], Thomas Wiemann [aut, cre]
Maintainer: Thomas Wiemann <wiemann at uchicago.edu>
BugReports: https://github.com/thomaswiemann/ddml/issues
License: GPL (≥ 3)
URL: https://github.com/thomaswiemann/ddml, https://thomaswiemann.com/ddml/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: ddml results


Reference manual: ddml.pdf
Vignettes: Get Started


Package source: ddml_0.2.2.tar.gz
Windows binaries: r-devel: ddml_0.2.2.zip, r-release: ddml_0.2.2.zip, r-oldrel: ddml_0.2.2.zip
macOS binaries: r-release (arm64): ddml_0.2.2.tgz, r-oldrel (arm64): ddml_0.2.2.tgz, r-release (x86_64): ddml_0.2.2.tgz, r-oldrel (x86_64): ddml_0.2.2.tgz
Old sources: ddml archive


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