Function reference • grf

Causal forest
`causal_forest()`	Causal forest
`causal_survival_forest()`	Causal survival forest
`multi_arm_causal_forest()`	Multi-arm/multi-outcome causal forest
`predict(<causal_forest>)`	Predict with a causal forest
`predict(<causal_survival_forest>)`	Predict with a causal survival forest forest
`predict(<multi_arm_causal_forest>)`	Predict with a multi arm causal forest
Instrumental forest
`instrumental_forest()`	Intrumental forest
`predict(<instrumental_forest>)`	Predict with an instrumental forest
Linear model forest
`lm_forest()`	LM Forest
`predict(<lm_forest>)`	Predict with a lm forest
Probability forest
`probability_forest()`	Probability forest
`predict(<probability_forest>)`	Predict with a probability forest
Quantile forest
`quantile_forest()`	Quantile forest
`predict(<quantile_forest>)`	Predict with a quantile forest
Regression forest
`regression_forest()`	Regression forest
`multi_regression_forest()`	Multi-task regression forest
`ll_regression_forest()`	Local linear forest
`boosted_regression_forest()`	Boosted regression forest
`predict(<regression_forest>)`	Predict with a regression forest
`predict(<multi_regression_forest>)`	Predict with a multi regression forest
`predict(<ll_regression_forest>)`	Predict with a local linear forest
`predict(<boosted_regression_forest>)`	Predict with a boosted regression forest.
Survival forest
`survival_forest()`	Survival forest
`predict(<survival_forest>)`	Predict with a survival forest
Treatment effect estimation Functions for estimating various treatment effects.
`average_treatment_effect()`	Get doubly robust estimates of average treatment effects.
`rank_average_treatment_effect()`	Estimate a Rank-Weighted Average Treatment Effect (RATE).
`best_linear_projection()`	Estimate the best linear projection of a conditional average treatment effect.
`get_scores(<causal_forest>)`	Compute doubly robust scores for a causal forest.
`get_scores(<multi_arm_causal_forest>)`	Compute doubly robust scores for a multi arm 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.
Analysis tools Functions for extracting further information from fitted forest objects.
`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_tree()`	Retrieve a single tree from a trained forest object.
`merge_forests()`	Merges a list of forests that were grown using the same data into one large forest.
`split_frequencies()`	Calculate which features the forest split on at each depth.
`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.
Plotting and printing
`plot(<grf_tree>)`	Plot a GRF tree object.
`plot(<rank_average_treatment_effect>)`	Plot the Targeting Operator Characteristic curve.
`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.
grf package options
`grf_options()`	grf package options

Reference

Causal forest

Instrumental forest

Linear model forest

Probability forest

Quantile forest

Regression forest

Survival forest

Treatment effect estimation

Analysis tools

Plotting and printing

grf package options