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policytree (version 1.2.3)

Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

Description

Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal.

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Install

install.packages('policytree')

Monthly Downloads

549

Version

1.2.3

License

MIT + file LICENSE

Issues

14

Pull Requests

0

Stars

78

Forks

14

Maintainer

Erik Sverdrup

Last Published

June 13th, 2024

Functions in policytree (1.2.3)

double_robust_scores.causal_forest

Matrix \(\Gamma\) of scores for each treatment \(a\)
export_graphviz

Export a tree in DOT format. This function generates a GraphViz representation of the tree, which is then written into dot_string.
make_tree

A utility function for generating random trees for test purposes.
hybrid_policy_tree

Hybrid tree search
print.policy_tree

Print a policy_tree object.
conditional_means.causal_forest

Estimate mean rewards \(\mu\) for each treatment \(a\)
create_dot_body

Writes each node information If it is a leaf node: show it in different color, show number of samples, show leaf id If it is a non-leaf node: show its splitting variable and splitting value
multi_causal_forest

(deprecated) One vs. all causal forest for multiple treatment effect estimation
plot.policy_tree

Plot a policy_tree tree object.
gen_data_epl

Example data generating process from Policy Learning With Observational Data
gen_data_mapl

Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization
predict.policy_tree

Predict method for policy_tree
policy_tree

Fit a policy with exact tree search
predict_test_tree

Predict with the above test tree.
policytree-package

policytree: Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees