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:10.48550/arXiv.2401.01645>, which leverages multiple base
learners to increase robustness to the underlying data generating process.
| Version: |
0.2.0 |
| 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: |
2024-01-09 |
| 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 |