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:10.1002/jae.3103>, which leverages multiple base learners to increase robustness to the underlying data generating process.
| Version: | 0.3.1 |
| Depends: | R (≥ 4.3) |
| Imports: | methods, stats,AER,MASS,Matrix,nnls,quadprog,glmnet,ranger,xgboost |
| Suggests: | sandwich,covr,testthat (≥ 3.0.0),knitr,rmarkdown |
| Published: | 2025-12-11 |
| 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 <thomas.wiemann at chicagobooth.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 |