Policy Learning
| Package name | Version | Title | Date | Size | License | |
|---|---|---|---|---|---|---|
| polle | 1.6.1 | Policy Learning | Mon Dec 01 2025 | 402.85kB | Apache License (>= 2) | |
| polle | 1.6.0 | Policy Learning | Thu Oct 30 2025 | 347.05kB | Apache License (>= 2) | |
| polle | 1.5 | Policy Learning | Fri Sep 06 2024 | 241.48kB | Apache License (>= 2) | |
| polle | 1.4 | Policy Learning | Thu Apr 25 2024 | 140.19kB | Apache License (>= 2) | |
| polle | 1.3 | Policy Learning | Thu Jul 06 2023 | 99.72kB | Apache License (>= 2) | |
| polle | 1.2 | Policy Learning | Tue Feb 07 2023 | 88.51kB | Apache License (>= 2) | |
| polle | 1.0 | Policy Learning | Tue Dec 06 2022 | 67.81kB | Apache License (>= 2) | |
| polle | 0.1 | Policy Learning | Mon Oct 31 2022 | 59.47kB | Apache License (>= 2) |
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.