Get doubly robust estimates of average treatment effects.
Estimate the best linear projection of a conditional average treatment effect.
Custom forest (removed)
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
If trained with missing values, the edge arrow is filled according to which direction the NAs are sent.
boosted_regression_forest
Boosted regression forest
Simple clustered bootstrap.
Causal survival forest
Causal forest
Average LATE (removed)
Average partial effect (removed)
generate_causal_survival_data
Simulate causal survival data
Compute doubly robust scores for a GRF forest object
Given a trained forest and test data, compute the kernel weights for each test point.
Compute E[T | X]
Compute doubly robust scores for a causal forest.
Generate causal forest data
Compute rate estimates, a function to be passed on to bootstrap routine.
Export a tree in DOT format.
This function generates a GraphViz representation of the tree,
which is then written into `dot_string`.
Find the leaf node for a test sample.
Retrieve forest weights (renamed to get_forest_weights)
Calculate summary stats given a set of samples for causal forests.
Intrumental 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.
Retrieve a single tree from a trained forest object.
leaf_stats.instrumental_forest
Calculate summary stats given a set of samples for instrumental forests.
grf: Generalized Random Forests
A default leaf_stats for forests classes without a leaf_stats method
that always returns NULL.
get_scores.instrumental_forest
Doubly robust scores for estimating the average conditional local average treatment effect.
leaf_stats.regression_forest
Calculate summary stats given a set of samples for regression forests.
predict.causal_survival_forest
Predict with a causal survival forest forest
Predict with a causal forest
Multi-task regression forest
Local linear forest
Plot a GRF tree object.
predict.boosted_regression_forest
Predict with a boosted regression forest.
Multi-arm/multi-outcome causal forest
plot.rank_average_treatment_effect
Plot the Targeting Operator Characteristic curve.
LM Forest
Merges a list of forests that were grown using the same data into one large forest.
predict.probability_forest
Predict with a probability forest
print.boosted_regression_forest
Print a boosted regression forest
predict.instrumental_forest
Predict with an instrumental forest
predict.multi_arm_causal_forest
Predict with a multi arm causal forest
predict.regression_forest
Predict with a regression forest
predict.ll_regression_forest
Predict with a local linear forest
Predict with a lm forest
Predict with a quantile forest
predict.multi_regression_forest
Predict with a multi regression forest
Predict with a survival forest
Regression forest
Calculate which features the forest split on at each depth.
Print a GRF forest object.
Print a GRF tree object.
rank_average_treatment_effect
Estimate a Rank-Weighted Average Treatment Effect (RATE).
Quantile forest
Probability forest
print.rank_average_treatment_effect
Print the Rank-Weighted Average Treatment Effect (RATE).
Print tuning output.
Displays average error for q-quantiles of tuned parameters.
rank_average_treatment_effect.fit
Fitter function for Rank-Weighted Average Treatment Effect (RATE).
Regression forest tuning (removed)
Survival forest
tune_ll_regression_forest
Local linear forest tuning
Causal forest tuning (removed)
Instrumental forest tuning (removed)
Omnibus evaluation of the quality of the random forest estimates via calibration.
Calculate a simple measure of 'importance' for each feature.
Tune a forest
Local linear forest tuning