Generalized Random Forests

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Documentation for package ‘grf’ version 2.3.2

• DESCRIPTION file.

Help Pages

average_late	Average LATE (removed)
average_partial_effect	Average partial effect (removed)
average_treatment_effect	Get doubly robust estimates of average treatment effects.
best_linear_projection	Estimate the best linear projection of a conditional average treatment effect.
boosted_regression_forest	Boosted regression forest
causal_forest	Causal forest
causal_survival_forest	Causal survival forest
custom_forest	Custom forest (removed)
generate_causal_data	Generate causal forest data
generate_causal_survival_data	Simulate causal survival data
get_forest_weights	Given a trained forest and test data, compute the kernel weights for each test point.
get_leaf_node	Find the leaf node for a test sample.
get_sample_weights	Retrieve forest weights (renamed to get_forest_weights)
get_scores	Compute doubly robust scores for a GRF forest object
get_scores.causal_forest	Compute doubly robust scores for a causal forest.
get_scores.causal_survival_forest	Compute doubly robust scores for a causal survival forest.
get_scores.instrumental_forest	Doubly robust scores for estimating the average conditional local average treatment effect.
get_scores.multi_arm_causal_forest	Compute doubly robust scores for a multi arm causal forest.
get_tree	Retrieve a single tree from a trained forest object.
instrumental_forest	Intrumental forest
ll_regression_forest	Local linear forest
lm_forest	LM Forest
merge_forests	Merges a list of forests that were grown using the same data into one large forest.
multi_arm_causal_forest	Multi-arm/multi-outcome causal forest
multi_regression_forest	Multi-task regression forest
plot.grf_tree	Plot a GRF tree object.
plot.rank_average_treatment_effect	Plot the Targeting Operator Characteristic curve.
predict.boosted_regression_forest	Predict with a boosted regression forest.
predict.causal_forest	Predict with a causal forest
predict.causal_survival_forest	Predict with a causal survival forest forest
predict.instrumental_forest	Predict with an instrumental forest
predict.ll_regression_forest	Predict with a local linear forest
predict.lm_forest	Predict with a lm forest
predict.multi_arm_causal_forest	Predict with a multi arm causal forest
predict.multi_regression_forest	Predict with a multi regression forest
predict.probability_forest	Predict with a probability forest
predict.quantile_forest	Predict with a quantile forest
predict.regression_forest	Predict with a regression forest
predict.survival_forest	Predict with a survival forest
print.boosted_regression_forest	Print a boosted regression forest
print.grf	Print a GRF forest object.
print.grf_tree	Print a GRF tree object.
print.rank_average_treatment_effect	Print the Rank-Weighted Average Treatment Effect (RATE).
print.tuning_output	Print tuning output. Displays average error for q-quantiles of tuned parameters.
probability_forest	Probability forest
quantile_forest	Quantile forest
rank_average_treatment_effect	Estimate a Rank-Weighted Average Treatment Effect (RATE).
rank_average_treatment_effect.fit	Fitter function for Rank-Weighted Average Treatment Effect (RATE).
regression_forest	Regression forest
split_frequencies	Calculate which features the forest split on at each depth.
survival_forest	Survival forest
test_calibration	Omnibus evaluation of the quality of the random forest estimates via calibration.
tune_causal_forest	Causal forest tuning (removed)
tune_instrumental_forest	Instrumental forest tuning (removed)
tune_regression_forest	Regression forest tuning (removed)
variable_importance	Calculate a simple measure of 'importance' for each feature.