Generalized Random Forests
Get doubly robust estimates of average treatment effects.
Estimate the best linear projection of a conditional average treatment...
Boosted regression forest
Simple clustered bootstrap.
Causal forest
Causal survival forest
Writes each node information If it is a leaf node: show it in differen...
Compute rate estimates, a function to be passed on to bootstrap routin...
Compute E[T | X]
Export a tree in DOT format. This function generates a GraphViz repres...
Generate causal forest data
Simulate causal survival data
Given a trained forest and test data, compute the kernel weights for e...
Find the leaf node for a test sample.
Compute doubly robust scores for a causal forest.
Compute doubly robust scores for a causal survival forest.
Doubly robust scores for estimating the average conditional local aver...
Compute doubly robust scores for a multi arm causal forest.
Compute doubly robust scores for a GRF forest object
Retrieve a single tree from a trained forest object.
grf package options
grf: Generalized Random Forests
Intrumental forest
Calculate summary stats given a set of samples for causal forests.
A default leaf_stats for forests classes without a leaf_stats method t...
Calculate summary stats given a set of samples for instrumental forest...
Calculate summary stats given a set of samples for regression forests.
Local linear forest
LM Forest
Merges a list of forests that were grown using the same data into one ...
Multi-arm/multi-outcome causal forest
Multi-task regression forest
Plot a GRF tree object.
Plot the Targeting Operator Characteristic curve.
Predict with a boosted regression forest.
Predict with a causal forest
Predict with a causal survival forest forest
Predict with an instrumental forest
Predict with a local linear forest
Predict with a lm forest
Predict with a multi arm causal forest
Predict with a multi regression forest
Predict with a probability forest
Predict with a quantile forest
Predict with a regression forest
Predict with a survival forest
Print a boosted regression forest
Print a GRF tree object.
Print a GRF forest object.
Print the Rank-Weighted Average Treatment Effect (RATE).
Print tuning output. Displays average error for q-quantiles of tuned p...
Probability forest
Quantile forest
Fitter function for Rank-Weighted Average Treatment Effect (RATE).
Estimate a Rank-Weighted Average Treatment Effect (RATE).
Regression forest
Calculate which features the forest split on at each depth.
Survival forest
Omnibus evaluation of the quality of the random forest estimates via c...
Tune a forest
Local linear forest tuning
Local linear forest tuning
Calculate a simple measure of 'importance' for each feature.
Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.