Generalized Random Forests

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Documentation for package ‘grf’ version 2.1.0

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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
merge_forests Merges a list of forests that were grown using the same data into one large forest.
multi_arm_causal_forest Multi-arm 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.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).
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.