This repository is in an 'beta' state, and is actively under development. We expect to make continual improvements to performance and usability.
This package is written and maintained by Julie Tibshirani (jtibs@cs.stanford.edu), Susan Athey, and Stefan Wager.
The repository first started as a fork of the ranger repository -- we owe a great deal of thanks to the ranger authors for their useful and free package.
The latest release of the package can be installed through CRAN:
install.packages("grf")Any published release can also be installed from source:
install.packages("https://raw.github.com/swager/grf/master/releases/grf_0.9.3.tar.gz", repos = NULL, type = "source")Note that to install from source, a compiler that implements C++11 is required (clang 3.3 or higher, or g++ 4.8 or higher). If installing on Windows, the RTools toolchain is also required.
library(grf)
# 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)
# Perform treatment effect estimation.
W = rbinom(n, 1, 0.5)
Y = pmax(X[,1], 0) * W + X[,2] + pmin(X[,3], 0) + rnorm(n)
tau.forest = causal_forest(X, Y, W)
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).
estimate_average_effect(tau.forest, target.sample = "all")
# Estimate the conditional average treatment effect on the treated sample (CATT).
# Here, we don't expect much difference between the CATE and the CATT, since
# treatment assignment was randomized.
estimate_average_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$variance.estimates)
plot(X.test[,1], tau.hat$predictions, ylim = range(tau.hat$predictions + 1.96 * sigma.hat, tau.hat$predictions - 1.96 * sigma.hat, 0, 2), 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)For examples on how to use other types of forest, including those for quantile regression and causal effect estimation using instrumental variables, please see the documentation directory.
In addition to providing out-of-the-box forests for quantile regression and causal effect estimation, grf provides a framework for creating forests tailored to new statistical tasks. If you'd like to develop using grf, please consult the development guide.
Susan Athey, Julie Tibshirani and Stefan Wager. Generalized Random Forests, 2016. [arxiv]