average_partial_effect
                        Estimate average partial effects using a causal
                        forest
average_treatment_effect
                        Estimate average treatment effects using a
                        causal forest
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
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
local_linear_forest     Local Linear forest
plot.grf_tree           Plot a GRF tree object.
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.local_linear_forest
                        Predict with a local linear forest
predict.quantile_forest
                        Predict with a quantile forest
predict.regression_forest
                        Predict with a regression forest
print.grf               Print a GRF forest object.
print.grf_tree          Print a GRF tree object.
quantile_forest         Quantile forest
regression_forest       Regression 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.
tune_causal_forest      Causal forest tuning
tune_local_linear_forest
                        Local linear forest tuning
tune_regression_forest
                        Regression forest tuning
variable_importance     Calculate a simple measure of 'importance' for
                        each feature.
