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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: | 0.3.0 |
| 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: | 2024-10-02 |
| 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 (source) |
| Package source: | ddml_0.3.0.tar.gz |
| Windows binaries: | r-devel: ddml_0.3.0.zip, r-release: ddml_0.3.0.zip, r-oldrel: ddml_0.3.0.zip |
| macOS binaries: | r-release (arm64): ddml_0.3.0.tgz, r-oldrel (arm64): ddml_0.3.0.tgz, r-release (x86_64): ddml_0.3.0.tgz, r-oldrel (x86_64): ddml_0.3.0.tgz |
| Old sources: | ddml archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.