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

# Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

## Documentation for package ‘policytree’ version 1.2.4

- DESCRIPTION file

## Help Pages

conditional_means | Estimate mean rewards mu for each treatment a
conditional_means.causal_forest | Estimate mean rewards mu for each treatment a
conditional_means.causal_survival_forest | Estimate mean rewards mu for each treatment a
conditional_means.instrumental_forest | Estimate mean rewards mu for each treatment a
conditional_means.multi_arm_causal_forest | Estimate mean rewards mu for each treatment a
double_robust_scores | Matrix Gamma of scores for each treatment a
double_robust_scores.causal_forest | Matrix Gamma of scores for each treatment a
double_robust_scores.causal_survival_forest | Matrix Gamma of scores for each treatment a
double_robust_scores.instrumental_forest | Matrix Gamma of scores for each treatment a
double_robust_scores.multi_arm_causal_forest | Matrix Gamma of scores for each treatment a
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
hybrid_policy_tree | Hybrid tree search
multi_causal_forest | (deprecated) One vs. all causal forest for multiple treatment effect estimation
plot.policy_tree | Plot a policy_tree tree object.
policy_tree | Fit a policy with exact tree search
predict.policy_tree | Predict method for policy_tree
print.policy_tree | Print a policy_tree object.