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| 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 |
| custom_forest | Custom forest |
| 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 |
| 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.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. |
| tune_causal_forest | Causal forest tuning |
| tune_regression_forest | Regression forest tuning |
| variable_importance | Calculate a simple measure of 'importance' for each feature. |