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 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:
conda users can install from the conda-forge channel:
conda install -c conda-forge r-grf
The current development version can be installed from source using devtools.
devtools::install_github("grf-labs/grf", subdir = "r-package/grf")
Note that to install from source, a compiler that implements C++11 or later is required. If installing on Windows, the RTools toolchain is also required.
The following script demonstrates how to use GRF for heterogeneous treatment effect estimation. For examples of how to use other types of forests, please consult the R documentation on the relevant methods.
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) # 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) 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$predictions - 1.96 * sigma.hat, col = 1, lty = 2) lines(X.test[, 1], pmax(0, X.test[, 1]), col = 2, lty = 1) # In some examples, pre-fitting models for Y and W separately may # be helpful (e.g., if different models use different covariates). # In some applications, one may even want to get Y.hat and W.hat # using a completely different method (e.g., boosting). # Generate new data. n <- 4000 p <- 20 X <- matrix(rnorm(n * p), n, p) TAU <- 1 / (1 + exp(-X[, 3])) W <- rbinom(n, 1, 1 / (1 + exp(-X[, 1] - X[, 2]))) Y <- pmax(X[, 2] + X[, 3], 0) + rowMeans(X[, 4:6]) / 2 + W * TAU + rnorm(n) forest.W <- regression_forest(X, W, tune.parameters = "all") W.hat <- predict(forest.W)$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))
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 algorithm reference and development guide.
Development of GRF is supported by the National Institutes of Health, the National Science Foundation, the Sloan Foundation, the Office of Naval Research (Grant N00014-17-1-2131) and Schmidt Futures.
Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, and Ruoqing Zhu. Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests. Journal of the Royal Statistical Society: Series B, 85(2), 2023. [paper, arxiv]
Imke Mayer, Erik Sverdrup, Tobias Gauss, Jean-Denis Moyer, Stefan Wager and Julie Josse. Doubly Robust Treatment Effect Estimation with Missing Attributes. Annals of Applied Statistics, 14(3), 2020. [paper, arxiv]
Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, and Stefan Wager. Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects. 2021. [arxiv]