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On the recordRead 12 Oct 202622 facts
policytree
On the record
- Build requirements
- Installing from source requires a C++ compiler and the R packages Rcpp and BH; Windows source builds require Rtools.grf-labs.github.io · read 12 Oct 2026
- Deeper trees
- hybrid_policy_tree() uses a hybrid optimal and greedy approach to fit deeper trees.grf-labs.github.io · read 12 Oct 2026
- Deployment
- desktopgrf-labs.github.io · read 23 Sep 2026
- Installation
- The latest release is available through CRAN, and the development version can be installed from GitHub.grf-labs.github.io · read 12 Oct 2026
- Integration
- PolicyTree uses reward estimates from the grf R package.grf-labs.github.io · read 12 Oct 2026
- Intended application
- The package is intended for settings requiring interpretable predictions, such as medical treatment prescription.grf-labs.github.io · read 12 Oct 2026
- Intended users
- The stated applications include settings that require interpretable predictions, such as medical treatment prescription.grf-labs.github.io · read 12 Oct 2026
- Larger datasets
- The project points to fastpolicytree and sparsepolicytree as modified solvers that may improve performance on larger datasets.grf-labs.github.io · read 12 Oct 2026
- Learning method
- It uses doubly robust reward estimates to find a shallow, globally optimal decision tree.grf-labs.github.io · read 12 Oct 2026
- License
- The package is distributed under the MIT license with an accompanying license file.grf-labs.github.io · read 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.grf-labs.github.io · read 12 Oct 2026
- Plotting
- Tree plotting requires the optional DiagrammeR package.github.com · read 12 Oct 2026
- Prediction
- A fitted tree can predict an action for new observations; predictions accept numeric matrices or data frames.github.com · read 12 Oct 2026
- Purpose
- PolicyTree learns simple rule based policies represented as shallow decision trees, including interpretable treatment prescriptions.grf-labs.github.io · read 12 Oct 2026
- Reward estimates
- The double_robust_scores function computes reward estimates for a subset of grf forest types.grf-labs.github.io · read 12 Oct 2026
- Scale considerations
- The project points to fastpolicytree and sparsepolicytree as modified solvers that may improve performance on larger datasets.grf-labs.github.io · read 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.grf-labs.github.io · read 12 Oct 2026
- Scripting support
- Yesgrf-labs.github.io · read 23 Sep 2026
