## Causal forest

causal_forest()

Causal forest

multi_arm_causal_forest()

Multi-arm causal forest (experimental)

predict(<causal_forest>)

Predict with a causal forest

predict(<multi_arm_causal_forest>)

Predict with a multi arm causal forest

## Causal survival forest

causal_survival_forest()

Causal survival forest (experimental)

predict(<causal_survival_forest>)

Predict with a causal survival forest forest

## Instrumental forest

instrumental_forest()

Intrumental forest

predict(<instrumental_forest>)

Predict with an instrumental 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()

ll_regression_forest()

Local linear forest

boosted_regression_forest()

Boosted regression forest (experimental)

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.

best_linear_projection()

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

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.

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.