Package: policytree 1.2.5
policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees
Learn optimal policies via doubly robust empirical welfare maximization over trees. Given reward estimates, the algorithm finds a rule-based treatment allocation, where the policy takes the form of a shallow decision tree that is globally optimal (or nearly so). Methods are described in Sverdrup, Kanodia, Zhou, Athey, and Wager (2020) <doi:10.21105/joss.02232>, Athey and Wager (2021) <doi:10.3982/ECTA15732>, and Zhou, Athey, and Wager (2023) <doi:10.1287/opre.2022.2271>.
Authors:
policytree_1.2.5.tar.gz
policytree_1.2.5.zip(r-4.7-x86_64)policytree_1.2.5.zip(r-4.7-arm64)policytree_1.2.5.zip(r-4.6-x86_64)policytree_1.2.5.zip(r-4.6-arm64)policytree_1.2.5.zip(r-4.5-x86_64)
policytree_1.2.5.tgz(r-4.6-x86_64)policytree_1.2.5.tgz(r-4.6-arm64)policytree_1.2.5.tgz(r-4.5-x86_64)policytree_1.2.5.tgz(r-4.5-arm64)
policytree_1.2.5.tar.gz(r-4.7-arm64)policytree_1.2.5.tar.gz(r-4.7-x86_64)policytree_1.2.5.tar.gz(r-4.6-arm64)policytree_1.2.5.tar.gz(r-4.6-x86_64)
policytree_1.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
policytree/json (API)
| # Install 'policytree' in R: |
| install.packages('policytree', repos = c('https://erikcs.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/grf-labs/policytree/issues
Last updated from:9919d4b13e. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 149 | ||
| linux-devel-x86_64 | OK | 149 | ||
| source / vignettes | OK | 177 | ||
| linux-release-arm64 | OK | 119 | ||
| linux-release-x86_64 | OK | 133 | ||
| macos-release-arm64 | OK | 165 | ||
| macos-release-x86_64 | OK | 375 | ||
| macos-oldrel-arm64 | OK | 170 | ||
| macos-oldrel-x86_64 | OK | 246 | ||
| windows-devel-arm64 | OK | 319 | ||
| windows-devel-x86_64 | OK | 154 | ||
| windows-release-arm64 | OK | 332 | ||
| windows-release-x86_64 | OK | 108 | ||
| windows-oldrel-x86_64 | OK | 107 | ||
| wasm-release | OK | 113 |
Exports:conditional_meansdouble_robust_scoresgen_data_eplgen_data_maplhybrid_policy_treemulti_causal_forestpolicy_tree
Dependencies:BHDiceKriginggrflatticelmtestMatrixRcppRcppEigensandwichzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate mean rewards mu for each treatment a | conditional_means conditional_means.causal_forest conditional_means.causal_survival_forest conditional_means.instrumental_forest conditional_means.multi_arm_causal_forest |
| Matrix Gamma of scores for each treatment a | double_robust_scores double_robust_scores.causal_forest double_robust_scores.causal_survival_forest double_robust_scores.instrumental_forest double_robust_scores.multi_arm_causal_forest |
| Example data generating process from Policy Learning With Observational Data | gen_data_epl |
| Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization | gen_data_mapl |
| Hybrid tree search | hybrid_policy_tree |
| (deprecated) One vs. all causal forest for multiple treatment effect estimation | multi_causal_forest |
| Plot a policy_tree tree object. | plot.policy_tree |
| Fit a policy with exact tree search | policy_tree |
| Predict method for policy_tree | predict.policy_tree |
| Print a policy_tree object. | print.policy_tree |