policytree
The facts below are what the maker publishes about policytree, checked and dated. Our editors have not written about it yet.
The facts
Each one read from policytree's own pages, with the date it was checked.
What it does
- Build requirements
- Installing from source requires a C++ compiler and the R packages Rcpp and BH; Windows source builds require Rtools.checked 12 Oct 2026
- Deeper trees
- hybrid_policy_tree() uses a hybrid optimal and greedy approach to fit deeper trees.checked 12 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- Installation
- The latest release is available through CRAN, and the development version can be installed from GitHub.checked 12 Oct 2026
- Integration
- PolicyTree uses reward estimates from the grf R package.checked 12 Oct 2026
- Intended users
- The stated applications include settings that require interpretable predictions, such as medical treatment prescription.checked 12 Oct 2026
- Larger datasets
- The project points to fastpolicytree and sparsepolicytree as modified solvers that may improve performance on larger datasets.checked 12 Oct 2026
- Learning method
- It uses doubly robust reward estimates to find a shallow, globally optimal decision tree.checked 12 Oct 2026
- License
- The package is distributed under the MIT license with an accompanying license file.checked 12 Oct 2026
- Optimization
- policy_tree() uses exhaustive search to fit a globally optimal tree of a specified depth that maximizes the sum of rewards.checked 12 Oct 2026
- Plotting
- Tree plotting requires the optional DiagrammeR package.checked 12 Oct 2026
- Prediction
- A fitted tree can predict an action for new observations; predictions accept numeric matrices or data frames.checked 12 Oct 2026
- Purpose
- PolicyTree learns simple rule based policies represented as shallow decision trees, including interpretable treatment prescriptions.checked 12 Oct 2026
- Reward estimates
- The double_robust_scores function computes reward estimates for a subset of grf forest types.checked 12 Oct 2026
- Scale considerations
- The project points to fastpolicytree and sparsepolicytree as modified solvers that may improve performance on larger datasets.checked 12 Oct 2026
- Scale limits
- The documentation says exact tree search does not scale to arbitrary dataset sizes and gives runtime examples showing substantial growth with more observations.checked 12 Oct 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Support
- The project directs usage questions to its GitHub issues page and welcomes contributions.checked 12 Oct 2026
- Treatment types
- The documentation demonstrates policy learning for binary and multi-action treatments, as well as instrumental-variable examples.checked 12 Oct 2026
- Tree search
- The policy_tree function finds a depth k tree by exhaustive search; hybrid_policy_tree combines optimal and greedy approaches for deeper trees.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
Where it runs
- Intended application
- The package is intended for settings requiring interpretable predictions, such as medical treatment prescription.checked 12 Oct 2026
