grf (version 2.0.1)

grf-package: grf: Generalized Random Forests

Description

A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods for least-squares regression, quantile regression, survival regression, and treatment effect estimation (optionally using instrumental variables), with support for missing values.

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).

See Also

Useful links:

• https://github.com/grf-labs/grf

• Report bugs at https://github.com/grf-labs/grf/issues

Examples

# NOT RUN {
# The following script demonstrates how to use GRF for heterogeneous treatment
# effect estimation. For examples of how to use other types of forest, as for
# quantile regression and causal effect estimation using instrumental variables,
# 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")

# Check whether causal forest predictions are well calibrated.
test_calibration(tau.forest)
# }
# NOT RUN {
# }