average_late |
Average LATE (removed) |
average_partial_effect |
Average partial effect (removed) |
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. |
boosted_regression_forest |
Boosted regression forest |
causal_forest |
Causal forest |
causal_survival_forest |
Causal survival forest |
custom_forest |
Custom forest (removed) |
generate_causal_data |
Generate causal forest data |
generate_causal_survival_data |
Simulate causal survival data |
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_sample_weights |
Retrieve forest weights (renamed to get_forest_weights) |
get_scores |
Compute doubly robust scores for a GRF forest object |
get_scores.causal_forest |
Compute doubly robust scores for a 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. |
get_scores.multi_arm_causal_forest |
Compute doubly robust scores for a multi arm causal forest. |
get_tree |
Retrieve a single tree from a trained forest object. |
instrumental_forest |
Intrumental forest |
ll_regression_forest |
Local linear forest |
lm_forest |
LM Forest |
merge_forests |
Merges a list of forests that were grown using the same data into one large forest. |
multi_arm_causal_forest |
Multi-arm/multi-outcome causal forest |
multi_regression_forest |
Multi-task regression forest |
plot.grf_tree |
Plot a GRF tree object. |
plot.rank_average_treatment_effect |
Plot the Targeting Operator Characteristic curve. |
predict.boosted_regression_forest |
Predict with a boosted regression forest. |
predict.causal_forest |
Predict with a causal forest |
predict.causal_survival_forest |
Predict with a causal survival forest forest |
predict.instrumental_forest |
Predict with an instrumental forest |
predict.ll_regression_forest |
Predict with a local linear forest |
predict.lm_forest |
Predict with a lm forest |
predict.multi_arm_causal_forest |
Predict with a multi arm causal forest |
predict.multi_regression_forest |
Predict with a multi regression forest |
predict.probability_forest |
Predict with a probability 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 |
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.rank_average_treatment_effect |
Print the Rank-Weighted Average Treatment Effect (RATE). |
print.tuning_output |
Print tuning output. Displays average error for q-quantiles of tuned parameters. |
probability_forest |
Probability forest |
quantile_forest |
Quantile forest |
rank_average_treatment_effect |
Estimate a Rank-Weighted Average Treatment Effect (RATE). |
rank_average_treatment_effect.fit |
Fitter function for Rank-Weighted Average Treatment Effect (RATE). |
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 (removed) |
tune_instrumental_forest |
Instrumental forest tuning (removed) |
tune_regression_forest |
Regression forest tuning (removed) |
variable_importance |
Calculate a simple measure of 'importance' for each feature. |