![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
| 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. |