All notable changes to policytree will be documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
hybrid_policy_tree consistent with policy_tree (level first). #156
hybrid_policy_tree for building deeper trees by using policy_tree to look ahead search.depth (typically equal to 2) splits. #118
policy_tree in case the input type (Gamma, X) is beyond what is computationally feasible with exact tree search. #116, #129
double_robust_scores for GRF’s causal_survival_forest. #126
double_robust_scores. #125
IMPORTANT Some of these changes might cause small differences in results compared to previous releases, even if the same random seed is used.
policytree to use GRF version 2.0.0. The “one vs all” multi_causal_forest is deprecated and we instead utilize the new GRF estimator multi_arm_causal_forest which supports multiple treatment arms natively. multi_causal_forest will continue to work until the next release, but dispatches to multi_arm_causal_forest and emits a warning. Note that this allows for a drop-in replacement in workflows that rely on calls to double_robust_scores for policy learning, but not for workflows involving point predictions (predict(forest)) as the new GRF estimator will for K treatment arms predict a K-1 contrast matrix. #67
leaf.label argument to plot.policy_tree allowing custom treatment names. #60
"type = node.id" argument to predict.policy_tree returning the leaf node the test sample falls into. #78.min.node.size to policy_tree specifying the smallest permissible node size. #77
split.step approximation for discrete covariates by redefining to skip observations instead of unique values. Note: this may cause small differences in results compared to previous releases when using the split.step approximation parameter. #73
pow call to retain CRAN compatibility. #54
help(<package-name>) in the R package. #41