grf-package: grf: Generalized Random Forests
In grf: Generalized Random Forests

grf-package

R Documentation

grf: Generalized Random Forests

Description

A package for 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.

In addition, GRF supports 'honest' estimation (where one subset of the data is used for choosing splits, and another for populating the leaves of the tree), and confidence intervals for least-squares regression and treatment effect estimation.

Some helpful links for getting started:

* The R package documentation contains usage examples and method reference (https://grf-labs.github.io/grf/).

* The GRF reference gives a detailed description of the GRF algorithm and includes troubleshooting suggestions (https://grf-labs.github.io/grf/REFERENCE.html).

* For community questions and answers around usage, see Github issues labelled 'question' (https://github.com/grf-labs/grf/issues?q=label%3Aquestion).

Author(s)

Maintainer: Julie Tibshirani jtibs@cs.stanford.edu

Authors:

Susan Athey
Erik Sverdrup
Stefan Wager

Other contributors:

Rina Friedberg [contributor]
Vitor Hadad [contributor]
David Hirshberg [contributor]
Luke Miner [contributor]
Marvin Wright [contributor]

Examples


# The following script demonstrates how to use GRF for heterogeneous treatment
# effect estimation. For examples of how to use other types of forest,
# please consult the documentation on the relevant forest methods (quantile_forest,
# instrumental_forest, etc.).

# Generate data.
n <- 2000; p <- 10
X <- matrix(rnorm(n*p), n, p)
X.test <- matrix(0, 101, p)
X.test[,1] <- seq(-2, 2, length.out = 101)

# Train a causal forest.
W <- rbinom(n, 1, 0.4 + 0.2 * (X[,1] > 0))
Y <- pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n)
tau.forest <- causal_forest(X, Y, W)

# Estimate treatment effects for the training data using out-of-bag prediction.
tau.hat.oob <- predict(tau.forest)
hist(tau.hat.oob$predictions)

# Estimate treatment effects for the test sample.
tau.hat <- predict(tau.forest, X.test)
plot(X.test[,1], tau.hat$predictions, ylim = range(tau.hat$predictions, 0, 2),
	xlab = "x", ylab = "tau", type = "l")
lines(X.test[,1], pmax(0, X.test[,1]), col = 2, lty = 2)

# Estimate the conditional average treatment effect on the full sample (CATE).
average_treatment_effect(tau.forest, target.sample = "all")

# Estimate the conditional average treatment effect on the treated sample (CATT).
average_treatment_effect(tau.forest, target.sample = "treated")

# Add confidence intervals for heterogeneous treatment effects; growing more
# trees is now recommended.
tau.forest <- causal_forest(X, Y, W, num.trees = 4000)
tau.hat <- predict(tau.forest, X.test, estimate.variance = TRUE)
sigma.hat <- sqrt(tau.hat$variance.estimates)

ylim <- range(tau.hat$predictions + 1.96 * sigma.hat, tau.hat$predictions - 1.96 * sigma.hat, 0, 2)
plot(X.test[,1], tau.hat$predictions, ylim = ylim, xlab = "x", ylab = "tau", type = "l")
lines(X.test[,1], tau.hat$predictions + 1.96 * sigma.hat, col = 1, lty = 2)
lines(X.test[,1], tau.hat$predictions - 1.96 * sigma.hat, col = 1, lty = 2)
lines(X.test[,1], pmax(0, X.test[,1]), col = 2, lty = 1)

# In some examples, pre-fitting models for Y and W separately may
# be helpful (e.g., if different models use different covariates).
# In some applications, one may even want to get Y.hat and W.hat
# using a completely different method (e.g., boosting).

# Generate new data.
n <- 4000; p <- 20
X <- matrix(rnorm(n * p), n, p)
TAU <- 1 / (1 + exp(-X[, 3]))
W <- rbinom(n ,1, 1 / (1 + exp(-X[, 1] - X[, 2])))
Y <- pmax(X[, 2] + X[, 3], 0) + rowMeans(X[, 4:6]) / 2 + W * TAU + rnorm(n)

forest.W <- regression_forest(X, W, tune.parameters = "all")
W.hat <- predict(forest.W)$predictions

forest.Y <- regression_forest(X, Y, tune.parameters = "all")
Y.hat <- predict(forest.Y)$predictions

forest.Y.varimp <- variable_importance(forest.Y)

# Note: Forests may have a hard time when trained on very few variables
# (e.g., ncol(X) = 1, 2, or 3). We recommend not being too aggressive
# in selection.
selected.vars <- which(forest.Y.varimp / mean(forest.Y.varimp) > 0.2)

tau.forest <- causal_forest(X[, selected.vars], Y, W,
                           W.hat = W.hat, Y.hat = Y.hat,
                           tune.parameters = "all")

# See if a causal forest succeeded in capturing heterogeneity by plotting
# the TOC and calculating a 95% CI for the AUTOC.
train <- sample(1:n, n / 2)
train.forest <- causal_forest(X[train, ], Y[train], W[train])
eval.forest <- causal_forest(X[-train, ], Y[-train], W[-train])
rate <- rank_average_treatment_effect(eval.forest,
                                      predict(train.forest, X[-train, ])$predictions)
plot(rate)
paste("AUTOC:", round(rate$estimate, 2), "+/", round(1.96 * rate$std.err, 2))

grf documentation built on Oct. 1, 2023, 1:07 a.m.