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policytree (version 1.0.4)

Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

Description

Learn optimal policies via doubly robust empirical welfare maximization over trees. This package implements the multi-action doubly robust approach of Zhou, Athey and Wager (2018) in the case where we want to learn policies that belong to the class of depth k decision trees.

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Install

install.packages('policytree')

Monthly Downloads

603

Version

1.0.4

License

GPL-3

Issues

14

Pull Requests

0

Stars

80

Forks

14

Maintainer

Erik Sverdrup

Last Published

March 10th, 2021

Functions in policytree (1.0.4)

double_robust_scores.causal_forest

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

Fit a policy with exact tree search
gen_data_mapl

Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization
multi_causal_forest

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

Plot a policy_tree tree object.
conditional_means.causal_forest

Estimate mean rewards \(\mu\) for each treatment \(a\)
print.policy_tree

Print a policy_tree object.
print.multi_causal_forest

Print a multi_causal_forest object.
predict.policy_tree

Predict method for policy_tree
gen_data_epl

Example data generating process from Policy Learning With Observational Data
policytree-package

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

Predict with multi_causal_forest
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
export_graphviz

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