Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.
| Version: |
1.6.2 |
| Depends: |
R (≥ 4.1), SuperLearner |
| Imports: |
data.table (≥ 1.14.5), lava (≥ 1.7.2.1), future.apply, progressr, methods, policytree (≥ 1.2.0), survival, targeted (≥ 0.6), DynTxRegime |
| Suggests: |
DTRlearn2, glmnet (≥ 4.1-6), mets, mgcv, xgboost, knitr, ranger, rmarkdown, testthat (≥ 3.0), ggplot2 |
| Published: |
2025-12-04 |
| DOI: |
10.32614/CRAN.package.polle |
| Author: |
Andreas Nordland [aut, cre],
Klaus Holst  [aut] |
| Maintainer: |
Andreas Nordland <andreasnordland at gmail.com> |
| BugReports: |
https://github.com/AndreasNordland/polle/issues |
| License: |
Apache License (≥ 2) |
| NeedsCompilation: |
no |
| Citation: |
polle citation info |
| Materials: |
README, NEWS |
| In views: |
CausalInference |
| CRAN checks: |
polle results |