rdlearn: Safe Policy Learning under Regression Discontinuity Design with
Multiple Cutoffs
Implements safe policy learning under regression discontinuity designs
with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>.
The learned cutoffs are guaranteed to perform no worse than the existing
cutoffs in terms of overall outcomes. The 'rdlearn' package also includes
features for visualizing the learned cutoffs relative to the baseline and
conducting sensitivity analyses.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
nprobust, nnet, rdrobust, ggplot2, dplyr, glue, cli |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2025-01-29 |
| DOI: |
10.32614/CRAN.package.rdlearn |
| Author: |
Kentaro Kawato [cre, cph],
Yi Zhang [aut],
Soichiro Yamauchi [aut],
Eli Ben-Michael [aut],
Kosuke Imai [aut] |
| Maintainer: |
Kentaro Kawato <kentaro1358nohe at gmail.com> |
| BugReports: |
https://github.com/kkawato/rdlearn/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/kkawato/rdlearn |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
rdlearn results |
Documentation:
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