Policy Learning via Doubly Robust Empirical Welfare Maximization over Trees

Documentation for package ‘policytree’ version 0.9.2

conditional_means	Estimate mean rewards mu for each treatment a
conditional_means.causal_forest	Estimate mean rewards mu for each treatment a
conditional_means.instrumental_forest	Estimate mean rewards mu for each treatment a
conditional_means.multi_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.instrumental_forest	Matrix Gamma of scores for each treatment a
double_robust_scores.multi_causal_forest	Matrix Gamma of scores for each treatment a
gen_data_epl	Example data generating process from Efficient Policy Learning
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.
policy_tree	Fit a policy with exact tree search
predict.multi_causal_forest	Predict with multi_causal_forest
predict.policy_tree	Predict method for policy_tree
print.multi_causal_forest	Print a multi_causal_forest object.
print.policy_tree	Print a policy_tree object.