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grf

Generalized Random Forests

A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, survival regression and treatment effect estimation (optionally using instrumental variables), with support for missing values.

Functions (44)

average_late

Estimate the average (conditional) local average treatment effect using a causal forest.

average_partial_effect

Estimate average partial effects using a causal forest

average_treatment_effect

Estimate average treatment effects using a causal forest

best_linear_projection

Estimate the best linear projection of a conditional average treatment effect using a causal forest.

boosted_regression_forest

Boosted regression forest (experimental)

causal_forest

Causal forest

create_dot_body

Writes each node information If it is a leaf node: show it in different color, show number of samples, show leaf id If it is a non-leaf node: show its splitting variable and splitting value If trained with missing values, the edge arrow is filled according to which direction the NAs are sent.

custom_forest

Custom forest

export_graphviz

Export a tree in DOT format. This function generates a GraphViz representation of the tree, which is then written into 'dot_string'.

get_sample_weights

Given a trained forest and test data, compute the training sample weights for each test point.

get_tree

Retrieve a single tree from a trained forest object.

grf

GRF

instrumental_forest

Intrumental forest

leaf_stats.causal_forest

Calculate summary stats given a set of samples for causal forests.

leaf_stats.default

A default leaf_stats for forests classes without a leaf_stats method that always returns NULL.

leaf_stats.instrumental_forest

Calculate summary stats given a set of samples for instrumental forests.

leaf_stats.regression_forest

Calculate summary stats given a set of samples for regression forests.

ll_regression_forest

Local Linear forest

merge_forests

Merges a list of forests that were grown using the same data into one large forest.

plot.grf_tree

Plot a GRF tree object.

predict.boosted_regression_forest

Predict with a boosted regression forest.

predict.causal_forest

Predict with a causal forest

predict.custom_forest

Predict with a custom forest.

predict.instrumental_forest

Predict with an instrumental forest

predict.ll_regression_forest

Predict with a local linear 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 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.tuning_output

Print tuning output. Displays average error for q-quantiles of tuned parameters.

quantile_forest

Quantile forest

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

tune_forest

Tune a forests

tune_instrumental_forest

Instrumental forest tuning

tune_ll_causal_forest

Local linear forest tuning

tune_ll_regression_forest

Local linear forest tuning

tune_regression_forest

Regression forest tuning

variable_importance

Calculate a simple measure of 'importance' for each feature.

grf

Generalized Random Forests

v1.2.0

GPL-3

Authors

Julie Tibshirani [aut, cre], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [ctb], Stefan Wager [aut], Marvin Wright [ctb]

Initial release

grf

Functions (44)

grf

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