# Generalized Random Forests

## Documentation for package ‘grf’ version 2.4.0

- DESCRIPTION file.

## Help Pages

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
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_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.
grf_options | grf package options
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.
variable_importance | Calculate a simple measure of 'importance' for each feature.