Estimate the best linear projection of a conditional average treatment effect
using a causal forest, or causal survival forest.
Average partial effect (removed)
Causal survival forest (experimental)
Custom forest (removed)
boosted_regression_forest
Boosted regression forest (experimental)
Get doubly robust estimates of average treatment effects.
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.
Average LATE (removed)
Causal forest
Compute pseudo outcomes (based on the influence function of tau(X) for each unit)
used for CART splitting.
Compute E[T | X]
Export a tree in DOT format.
This function generates a GraphViz representation of the tree,
which is then written into `dot_string`.
generate_causal_survival_data
Simulate causal survival data
Generate causal forest data
leaf_stats.regression_forest
Calculate summary stats given a set of samples for regression forests.
Compute doubly robust scores for a causal forest.
get_scores.causal_survival_forest
Compute doubly robust scores for a causal survival forest.
Local linear forest
Calculate summary stats given a set of samples for causal forests.
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.
Compute doubly robust scores for a GRF forest object
leaf_stats.instrumental_forest
Calculate summary stats given a set of samples for instrumental forests.
Retrieve forest weights (renamed to get_forest_weights)
A default leaf_stats for forests classes without a leaf_stats method
that always returns NULL.
predict.ll_regression_forest
Predict with a local linear forest
predict.multi_arm_causal_forest
Predict with a multi arm causal forest
predict.instrumental_forest
Predict with an instrumental forest
predict.causal_survival_forest
Predict with a causal survival forest forest
Predict with a quantile forest
Merges a list of forests that were grown using the same data into one large forest.
predict.regression_forest
Predict with a regression forest
predict.boosted_regression_forest
Predict with a boosted regression forest.
Predict with a causal forest
Probability forest
Print tuning output.
Displays average error for q-quantiles of tuned parameters.
Multi-arm causal forest (experimental)
Given a trained forest and test data, compute the kernel weights for each test point.
Tune a forest
Intrumental forest
Causal forest tuning (removed)
Omnibus evaluation of the quality of the random forest estimates via calibration.
predict.multi_regression_forest
Predict with a multi regression forest
Instrumental forest tuning (removed)
print.boosted_regression_forest
Print a boosted regression forest
tune_ll_regression_forest
Local linear forest tuning
Predict with a survival forest
Local linear forest tuning
Print a GRF forest object.
Survival forest
Print a GRF tree object.
Calculate which features the forest split on at each depth.
Retrieve a single tree from a trained forest object.
Find the leaf node for a test sample.
Regression forest tuning (removed)
Regression forest
Multi-task regression forest
grf: Generalized Random Forests
Plot a GRF tree object.
Quantile forest
predict.probability_forest
Predict with a probability forest
Calculate a simple measure of 'importance' for each feature.