Package 'grf'

Description

See the function 'average_treatment_effect'

Usage

average_late(forest, ...)

Arguments

Value

output

Description

See the function 'average_treatment_effect'

Usage

average_partial_effect(forest, ...)

Arguments

Value

output

Description

In the case of a causal forest with binary treatment, we provide estimates of one of the following:

• The average treatment effect (target.sample = all): E[Y(1) - Y(0)]

• The average treatment effect on the treated (target.sample = treated): E[Y(1) - Y(0) | Wi = 1]

• The average treatment effect on the controls (target.sample = control): E[Y(1) - Y(0) | Wi = 0]

• The overlap-weighted average treatment effect (target.sample = overlap): E[e(X) (1 - e(X)) (Y(1) - Y(0))] / E[e(X) (1 - e(X)), where e(x) = P[Wi = 1 | Xi = x].

This last estimand is recommended by Li, Morgan, and Zaslavsky (2018) in case of poor overlap (i.e., when the propensities e(x) may be very close to 0 or 1), as it doesn't involve dividing by estimated propensities.

Usage

average_treatment_effect(
  forest,
  target.sample = c("all", "treated", "control", "overlap"),
  method = c("AIPW", "TMLE"),
  subset = NULL,
  debiasing.weights = NULL,
  compliance.score = NULL,
  num.trees.for.weights = 500
)

Arguments

Details

In the case of a causal forest with continuous treatment, we provide estimates of the average partial effect, i.e., E[Cov[W, Y | X] / Var[W | X]]. In the case of a binary treatment, the average partial effect matches the average treatment effect. Computing the average partial effect is somewhat more involved, as the relevant doubly robust scores require an estimate of Var[Wi | Xi = x]. By default, we get such estimates by training an auxiliary forest; however, these weights can also be passed manually by specifying debiasing.weights.

In the case of instrumental forests with a binary treatment, we provide an estimate of the the Average (Conditional) Local Average Treatment (ACLATE). Specifically, given an outcome Y, treatment W and instrument Z, the (conditional) local average treatment effect is tau(x) = Cov[Y, Z | X = x] / Cov[W, Z | X = x]. This is the quantity that is estimated with an instrumental forest. It can be intepreted causally in various ways. Given a homogeneity assumption, tau(x) is simply the CATE at x. When W is binary and there are no "defiers", Imbens and Angrist (1994) show that tau(x) can be interpreted as an average treatment effect on compliers. This function provides and estimate of tau = E[tau(X)]. See Chernozhukov et al. (2016) for a discussion, and Section 5.2 of Athey and Wager (2021) for an example using forests.

If clusters are specified, then each unit gets equal weight by default. For example, if there are 10 clusters with 1 unit each and per-cluster ATE = 1, and there are 10 clusters with 19 units each and per-cluster ATE = 0, then the overall ATE is 0.05 (additional sample.weights allow for custom weighting). If equalize.cluster.weights = TRUE each cluster gets equal weight and the overall ATE is 0.5.

Value

An estimate of the average treatment effect, along with standard error.

References

Athey, Susan, and Stefan Wager. "Policy Learning With Observational Data." Econometrica 89.1 (2021): 133-161.

Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally robust semiparametric estimation." Econometrica 90(4), 2022.

Imbens, Guido W., and Joshua D. Angrist. "Identification and Estimation of Local Average Treatment Effects." Econometrica 62(2), 1994.

Li, Fan, Kari Lock Morgan, and Alan M. Zaslavsky. "Balancing covariates via propensity score weighting." Journal of the American Statistical Association 113(521), 2018.

Mayer, Imke, 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.

Robins, James M., and Andrea Rotnitzky. "Semiparametric efficiency in multivariate regression models with missing data." Journal of the American Statistical Association 90(429), 1995.

Examples

# Train a causal forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
c.forest <- causal_forest(X, Y, W)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
c.pred <- predict(c.forest, X.test)
# Estimate the conditional average treatment effect on the full sample (CATE).
average_treatment_effect(c.forest, target.sample = "all")

# Estimate the conditional average treatment effect on the treated sample (CATT).
# We don't expect much difference between the CATE and the CATT in this example,
# since treatment assignment was randomized.
average_treatment_effect(c.forest, target.sample = "treated")

# Estimate the conditional average treatment effect on samples with positive X[,1].
average_treatment_effect(c.forest, target.sample = "all", subset = X[, 1] > 0)

# Example for causal forests with a continuous treatment.
n <- 2000
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 1 / (1 + exp(-X[, 2]))) + rnorm(n)
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)
average_treatment_effect(tau.forest)
average_treatment_effect(tau.forest, subset = X[, 1] > 0)

Description

Let tau(Xi) = E[Y(1) - Y(0) | X = Xi] be the CATE, and Ai be a vector of user-provided covariates. This function provides a (doubly robust) fit to the linear model tau(Xi) ~ beta_0 + Ai * beta.

Usage

best_linear_projection(
  forest,
  A = NULL,
  subset = NULL,
  debiasing.weights = NULL,
  compliance.score = NULL,
  num.trees.for.weights = 500,
  vcov.type = "HC3",
  target.sample = c("all", "overlap")
)

Arguments

Details

Procedurally, we do so by regressing doubly robust scores derived from the forest against the Ai. Note the covariates Ai may consist of a subset of the Xi, or they may be distinct. The case of the null model tau(Xi) ~ beta_0 is equivalent to fitting an average treatment effect via AIPW.

In the event the treatment is continuous the inverse-propensity weight component of the double robust scores are replaced with a component based on a forest based estimate of Var[Wi | Xi = x]. These weights can also be passed manually by specifying debiasing.weights.

Value

An estimate of the best linear projection, along with coefficient standard errors.

References

Cameron, A. Colin, and Douglas L. Miller. "A practitioner's guide to cluster-robust inference." Journal of Human Resources 50, no. 2 (2015): 317-372.

Cui, Yifan, 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.

MacKinnon, James G., and Halbert White. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties." Journal of Econometrics 29.3 (1985): 305-325.

Semenova, Vira, and Victor Chernozhukov. "Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions". The Econometrics Journal 24.2 (2021).

Examples

n <- 800
p <- 5
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.25 + 0.5 * (X[, 1] > 0))
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
forest <- causal_forest(X, Y, W)
best_linear_projection(forest, X[,1:2])

Description

Trains a boosted regression forest that can be used to estimate the conditional mean function mu(x) = E[Y | X = x]. Selects number of boosting iterations based on cross-validation.

Usage

boosted_regression_forest(
  X,
  Y,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  ci.group.size = 2,
  tune.parameters = "none",
  tune.num.trees = 10,
  tune.num.reps = 100,
  tune.num.draws = 1000,
  boost.steps = NULL,
  boost.error.reduction = 0.97,
  boost.max.steps = 5,
  boost.trees.tune = 10,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A boosted regression forest object. $error contains the mean debiased error for each step, and $forests contains the trained regression forest for each step.

Examples

# Train a boosted regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
boosted.forest <- boosted_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
boost.pred <- predict(boosted.forest, X.test)

# Predict on out-of-bag training samples.
boost.pred <- predict(boosted.forest)

# Check how many boosting iterations were used
print(length(boosted.forest$forests))

Description

Trains a causal forest that can be used to estimate conditional average treatment effects tau(X). When the treatment assignment W is binary and unconfounded, we have tau(X) = E[Y(1) - Y(0) | X = x], where Y(0) and Y(1) are potential outcomes corresponding to the two possible treatment states. When W is continuous, we effectively estimate an average partial effect Cov[Y, W | X = x] / Var[W | X = x], and interpret it as a treatment effect given unconfoundedness.

Usage

causal_forest(
  X,
  Y,
  W,
  Y.hat = NULL,
  W.hat = NULL,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  stabilize.splits = TRUE,
  ci.group.size = 2,
  tune.parameters = "none",
  tune.num.trees = 200,
  tune.num.reps = 50,
  tune.num.draws = 1000,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained causal forest object. If tune.parameters is enabled, then tuning information will be included through the 'tuning.output' attribute.

References

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". Annals of Statistics, 47(2), 2019.

Wager, Stefan, and Susan Athey. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests". Journal of the American Statistical Association, 113(523), 2018.

Nie, Xinkun, and Stefan Wager. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects". Biometrika, 108(2), 2021.

Examples

# Train a causal forest.
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
c.forest <- causal_forest(X, Y, W)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
c.pred <- predict(c.forest, X.test)

# Predict on out-of-bag training samples.
c.pred <- predict(c.forest)

# Predict with confidence intervals; growing more trees is now recommended.
c.forest <- causal_forest(X, Y, W, num.trees = 4000)
c.pred <- predict(c.forest, X.test, estimate.variance = TRUE)

# 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).
n <- 2000
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"
)
tau.hat <- predict(tau.forest)$predictions

# 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))

Description

Trains a causal survival forest that can be used to estimate conditional treatment effects tau(X) with right-censored outcomes. We estimate either 1) tau(X) = E[min(T(1), horizon) - min(T(0), horizon) | X = x], where T(1) and T(0) are potental outcomes corresponding to the two possible treatment states and 'horizon' is the maximum follow-up time, or 2) tau(X) = P[T(1) > horizon | X = x] - P[T(0) > horizon | X = x], for a chosen time point 'horizon'.

Usage

causal_survival_forest(
  X,
  Y,
  W,
  D,
  W.hat = NULL,
  target = c("RMST", "survival.probability"),
  horizon = NULL,
  failure.times = NULL,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  stabilize.splits = TRUE,
  ci.group.size = 2,
  tune.parameters = "none",
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Details

When W is continuous, we effectively estimate an average partial effect corresponding to 1) Cov[min(T, horizon), W | X = x] / Var[W | X = x] or 2) Cov[1(T > horizon), W | X = x] / Var[W | X = x], and interpret it as a treatment effect given unconfoundedness.

Value

A trained causal_survival_forest forest object.

References

Cui, Yifan, 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.

Sverdrup, Erik, and Stefan Wager. "Treatment Heterogeneity with Right-Censored Outcomes Using grf". ASA Lifetime Data Science Newsletter, January 2024 (arXiv:2312.02482).

Examples

# Train a causal survival forest targeting a Restricted Mean Survival Time (RMST)
# with maximum follow-up time set to `horizon`.
n <- 2000
p <- 5
X <- matrix(runif(n * p), n, p)
W <- rbinom(n, 1, 0.5)
horizon <- 1
failure.time <- pmin(rexp(n) * X[, 1] + W, horizon)
censor.time <- 2 * runif(n)
Y <- pmin(failure.time, censor.time)
D <- as.integer(failure.time <= censor.time)
# Save computation time by constraining the event grid by discretizing (rounding) continuous events.
cs.forest <- causal_survival_forest(X, round(Y, 2), W, D, horizon = horizon)
# Or do so more flexibly by defining your own time grid using the failure.times argument.
# grid <- seq(min(Y), max(Y), length.out = 150)
# cs.forest <- causal_survival_forest(X, Y, W, D, horizon = horizon, failure.times = grid)

# Predict using the forest.
X.test <- matrix(0.5, 10, p)
X.test[, 1] <- seq(0, 1, length.out = 10)
cs.pred <- predict(cs.forest, X.test)

# Predict on out-of-bag training samples.
cs.pred <- predict(cs.forest)

# Predict with confidence intervals; growing more trees is now recommended.
c.pred <- predict(cs.forest, X.test, estimate.variance = TRUE)

# Compute a doubly robust estimate of the average treatment effect.
average_treatment_effect(cs.forest)

# Compute the best linear projection on the first covariate.
best_linear_projection(cs.forest, X[, 1])

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

Description

To build a custom forest, see an existing simpler forest, like regression_forest, for a development template.

Usage

custom_forest(X, Y, ...)

Arguments

Description

The following DGPs are available for benchmarking purposes:

• "simple": tau = max(X1, 0), e = 0.4 + 0.2 * 1(X1 > 0).

• "aw1": equation (27) of https://arxiv.org/pdf/1510.04342.pdf

• "aw2": equation (28) of https://arxiv.org/pdf/1510.04342.pdf

• "aw3": confounding is from "aw1" and tau is from "aw2"

• "aw3reverse": Same as aw3, but HTEs anticorrelated with baseline

• "ai1": "Setup 1" from section 6 of https://arxiv.org/pdf/1504.01132.pdf

• "ai2": "Setup 2" from section 6 of https://arxiv.org/pdf/1504.01132.pdf

• "kunzel": "Simulation 1" from A.1 in https://arxiv.org/pdf/1706.03461.pdf

• "nw1": "Setup A" from Section 4 of https://arxiv.org/pdf/1712.04912.pdf

• "nw2": "Setup B" from Section 4 of https://arxiv.org/pdf/1712.04912.pdf

• "nw3": "Setup C" from Section 4 of https://arxiv.org/pdf/1712.04912.pdf

• "nw4": "Setup D" from Section 4 of https://arxiv.org/pdf/1712.04912.pdf

Usage

generate_causal_data(
  n,
  p,
  sigma.m = 1,
  sigma.tau = 0.1,
  sigma.noise = 1,
  dgp = c("simple", "aw1", "aw2", "aw3", "aw3reverse", "ai1", "ai2", "kunzel", "nw1",
    "nw2", "nw3", "nw4")
)

Arguments

Details

Each DGP is parameterized by X: observables, m: conditional mean of Y, tau: treatment effect, e: propensity scores, V: conditional variance of Y.

The following rescaled data is returned m = m / sd(m) * sigma.m, tau = tau / sd(tau) * sigma.tau, V = V / mean(V) * sigma.noise^2, W = rbinom(e), Y = m + (W - e) * tau + sqrt(V) + rnorm(n).

Value

A list consisting of: X, Y, W, tau, m, e, dgp.

Examples

# Generate simple benchmark data
data <- generate_causal_data(100, 5, dgp = "simple")
# Generate data from Wager and Athey (2018)
data <- generate_causal_data(100, 5, dgp = "aw1")
data2 <- generate_causal_data(100, 5, dgp = "aw2")

Description

The following DGPs are available for benchmarking purposes, T is the failure time and C the censoring time:

• "simple1": T = X1*eps + W, C ~ U(0, 2) where eps ~ Exp(1) and Y.max = 1.

• "type1": T is drawn from an accelerated failure time model and C from a Cox model (scenario 1 in https://arxiv.org/abs/2001.09887)

• "type2": T is drawn from a proportional hazard model and C from a accelerated failure time (scenario 2 in https://arxiv.org/abs/2001.09887)

• "type3": T and C are drawn from a Poisson distribution (scenario 3 in https://arxiv.org/abs/2001.09887)

• "type4": T and C are drawn from a Poisson distribution (scenario 4 in https://arxiv.org/abs/2001.09887)

• "type5": is similar to "type2" but with censoring generated from an accelerated failure time model.

Usage

generate_causal_survival_data(
  n,
  p,
  Y.max = NULL,
  y0 = NULL,
  X = NULL,
  rho = 0,
  n.mc = 10000,
  dgp = c("simple1", "type1", "type2", "type3", "type4", "type5")
)

Arguments

Value

A list with entries: 'X': the covariates, 'Y': the event times, 'W': the treatment indicator, 'D': the censoring indicator, 'cate': the treatment effect (RMST) estimated by monte carlo, 'cate.prob' the difference in survival probability, 'cate.sign': the true sign of the cate for ITR comparison, 'dgp': the dgp name, 'Y.max': the maximum follow-up time, 'y0': the query time for difference in survival probability.

Examples

# Generate data
n <- 1000
p <- 5
data <- generate_causal_survival_data(n, p)
# Get true CATE on a test set
X.test <- matrix(seq(0, 1, length.out = 5), 5, p)
cate.test <- generate_causal_survival_data(n, p, X = X.test)$cate

Description

During normal prediction, these weights (named alpha in the GRF paper) are computed as an intermediate step towards producing estimates. This function allows for examining the weights directly, so they could be potentially be used as the input to a different analysis.

Usage

get_forest_weights(forest, newdata = NULL, num.threads = NULL)

Arguments

Value

A sparse matrix where each row represents a test sample, and each column is a sample in the training data. The value at (i, j) gives the weight of training sample j for test sample i.

Examples

p <- 10
n <- 100
X <- matrix(2 * runif(n * p) - 1, n, p)
Y <- (X[, 1] > 0) + 2 * rnorm(n)
rrf <- regression_forest(X, Y, mtry = p)
forest.weights.oob <- get_forest_weights(rrf)

n.test <- 15
X.test <- matrix(2 * runif(n.test * p) - 1, n.test, p)
forest.weights <- get_forest_weights(rrf, X.test)

Description

Given a GRF tree object, compute the leaf node a test sample falls into. The nodes in a GRF tree are numbered breadth first, and the returned numbers will be the leaf integer according to this ordering. To get kernel weights based on leaf membership, see the function get_forest_weights.

Usage

get_leaf_node(tree, newdata, node.id = TRUE)

Arguments

Value

A vector of integers indicating the leaf number for each sample in the given tree.

Examples

p <- 10
n <- 100
X <- matrix(2 * runif(n * p) - 1, n, p)
Y <- (X[, 1] > 0) + 2 * rnorm(n)
r.forest <- regression_forest(X, Y, num.tree = 50)

n.test <- 5
X.test <- matrix(2 * runif(n.test * p) - 1, n.test, p)
tree <- get_tree(r.forest, 1)
# Get a vector of node numbers for each sample.
get_leaf_node(tree, X.test)
# Get a list of samples per node.
get_leaf_node(tree, X.test, node.id = FALSE)

Description

Retrieve forest weights (renamed to get_forest_weights)

Usage

get_sample_weights(forest, ...)

Arguments

Description

Compute doubly robust scores for a GRF forest object

Usage

get_scores(forest, ...)

Arguments

Value

A vector of scores

Description

Compute doubly robust (AIPW) scores for average treatment effect estimation or average partial effect estimation with continuous treatment, using a causal forest. Under regularity conditions, the average of the DR.scores is an efficient estimate of the average treatment effect.

Usage

## S3 method for class 'causal_forest'
get_scores(
  forest,
  subset = NULL,
  debiasing.weights = NULL,
  num.trees.for.weights = 500,
  ...
)

Arguments

Value

A vector of scores.

References

Farrell, Max H. "Robust inference on average treatment effects with possibly more covariates than observations." Journal of Econometrics 189(1), 2015.

Graham, Bryan S., and Cristine Campos de Xavier Pinto. "Semiparametrically efficient estimation of the average linear regression function." Journal of Econometrics 226(1), 2022.

Hirshberg, David A., and Stefan Wager. "Augmented minimax linear estimation." The Annals of Statistics 49(6), 2021.

Robins, James M., and Andrea Rotnitzky. "Semiparametric efficiency in multivariate regression models with missing data." Journal of the American Statistical Association 90(429), 1995.

Description

For details see section 3.2 in the causal survival forest paper.

Usage

## S3 method for class 'causal_survival_forest'
get_scores(forest, subset = NULL, num.trees.for.weights = 500, ...)

Arguments

Value

A vector of scores.

Description

Given an outcome Y, treatment W and instrument Z, the (conditional) local average treatment effect is tau(x) = Cov[Y, Z | X = x] / Cov[W, Z | X = x]. This is the quantity that is estimated with an instrumental forest. It can be intepreted causally in various ways. Given a homogeneity assumption, tau(x) is simply the CATE at x. When W is binary and there are no "defiers", Imbens and Angrist (1994) show that tau(x) can be interpreted as an average treatment effect on compliers. This doubly robust scores provided here are for estimating tau = E[tau(X)].

Usage

## S3 method for class 'instrumental_forest'
get_scores(
  forest,
  subset = NULL,
  debiasing.weights = NULL,
  compliance.score = NULL,
  num.trees.for.weights = 500,
  ...
)

Arguments

Value

A vector of scores.

References

Aronow, Peter M., and Allison Carnegie. "Beyond LATE: Estimation of the average treatment effect with an instrumental variable." Political Analysis 21(4), 2013.

Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K. Newey, and James M. Robins. "Locally robust semiparametric estimation." Econometrica 90(4), 2022.

Imbens, Guido W., and Joshua D. Angrist. "Identification and Estimation of Local Average Treatment Effects." Econometrica 62(2), 1994.

Description

Compute doubly robust (AIPW) scores for average treatment effect estimation using a multi arm causal forest. Under regularity conditions, the average of the DR.scores is an efficient estimate of the average treatment effect.

Usage

## S3 method for class 'multi_arm_causal_forest'
get_scores(forest, subset = NULL, drop = FALSE, ...)

Arguments

Value

An array of scores for each contrast and outcome.

Description

Retrieve a single tree from a trained forest object.

Usage

get_tree(forest, index)

Arguments

Value

A GRF tree object containing the below attributes. drawn_samples: a list of examples that were used in training the tree. This includes examples that were used in choosing splits, as well as the examples that populate the leaf nodes. Put another way, if honesty is enabled, this list includes both subsamples from the split (J1 and J2 in the notation of the paper). num_samples: the number of examples used in training the tree. nodes: a list of objects representing the nodes in the tree, starting with the root node. Each node will contain an 'is_leaf' attribute, which indicates whether it is an interior or leaf node. Interior nodes contain the attributes 'left_child' and 'right_child', which give the indices of their children in the list, as well as 'split_variable', and 'split_value', which describe the split that was chosen. Leaf nodes only have the attribute 'samples', which is a list of the training examples that the leaf contains. Note that if honesty is enabled, this list will only contain examples from the second subsample that was used to 'repopulate' the tree (J2 in the notation of the paper).

Examples

# Train a quantile forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
q.forest <- quantile_forest(X, Y, quantiles = c(0.1, 0.5, 0.9))

# Examine a particular tree.
q.tree <- get_tree(q.forest, 3)
q.tree$nodes

Description

Trains an instrumental forest that can be used to estimate conditional local average treatment effects tau(X) identified using instruments. Formally, the forest estimates tau(X) = Cov[Y, Z | X = x] / Cov[W, Z | X = x]. Note that when the instrument Z and treatment assignment W coincide, an instrumental forest is equivalent to a causal forest.

Usage

instrumental_forest(
  X,
  Y,
  W,
  Z,
  Y.hat = NULL,
  W.hat = NULL,
  Z.hat = NULL,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  stabilize.splits = TRUE,
  ci.group.size = 2,
  reduced.form.weight = 0,
  tune.parameters = "none",
  tune.num.trees = 200,
  tune.num.reps = 50,
  tune.num.draws = 1000,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained instrumental forest object.

References

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". Annals of Statistics, 47(2), 2019.

Examples

# Train an instrumental forest.
n <- 2000
p <- 5
X <- matrix(rbinom(n * p, 1, 0.5), n, p)
Z <- rbinom(n, 1, 0.5)
Q <- rbinom(n, 1, 0.5)
W <- Q * Z
tau <-  X[, 1] / 2
Y <- rowSums(X[, 1:3]) + tau * W + Q + rnorm(n)
iv.forest <- instrumental_forest(X, Y, W, Z)

# Predict on out-of-bag training samples.
iv.pred <- predict(iv.forest)

# Estimate a (local) average treatment effect.
average_treatment_effect(iv.forest)

Description

Trains a local linear forest that can be used to estimate the conditional mean function mu(x) = E[Y | X = x]

Usage

ll_regression_forest(
  X,
  Y,
  enable.ll.split = FALSE,
  ll.split.weight.penalty = FALSE,
  ll.split.lambda = 0.1,
  ll.split.variables = NULL,
  ll.split.cutoff = NULL,
  num.trees = 2000,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  ci.group.size = 2,
  tune.parameters = "none",
  tune.num.trees = 50,
  tune.num.reps = 100,
  tune.num.draws = 1000,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained local linear forest object.

References

Friedberg, Rina, Julie Tibshirani, Susan Athey, and Stefan Wager. "Local Linear Forests". Journal of Computational and Graphical Statistics, 30(2), 2020.

Examples

# Train a standard regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
forest <- ll_regression_forest(X, Y)

Description

Trains a linear model forest that can be used to estimate $h_k(x)$, k = 1..K at X = x in the the conditional linear model $Y = c(x) + h_1(x)W_1 + ... + h_K(x)W_K$, where Y is a (potentially vector-valued) response and W a set of regressors.

Usage

lm_forest(
  X,
  Y,
  W,
  Y.hat = NULL,
  W.hat = NULL,
  num.trees = 2000,
  sample.weights = NULL,
  gradient.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  stabilize.splits = FALSE,
  ci.group.size = 2,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained lm forest object.

References

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". Annals of Statistics, 47(2), 2019.

Zeileis, Achim, Torsten Hothorn, and Kurt Hornik. "Model-based Recursive Partitioning." Journal of Computational and Graphical Statistics 17(2), 2008.

Examples

if (require("rdd", quietly = TRUE)) {
# Train a LM Forest to estimate CATEs in a regression discontinuity design.
# Simulate a simple example with a heterogeneous jump in the CEF.
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
Z <- runif(n, -4, 4)
cutoff <- 0
W <- as.numeric(Z >= cutoff)
tau <- pmax(0.5 * X[, 1], 0)
Y <- tau * W  + 1 / (1 + exp(2 * Z)) + 0.2 * rnorm(n)

# Compute the Imbens-Kalyanaraman MSE-optimal bandwidth for a local linear regression.
bandwidth <- IKbandwidth(Z, Y, cutoff)
# Compute kernel weights for a triangular kernel.
sample.weights <- kernelwts(Z, cutoff, bandwidth, "triangular")

# Alternatively, specify bandwith and triangular kernel weights without using the `rdd` package.
# bandwidth <- # user can hand-specify this.
# dist <- abs((Z - cutoff) / bandwidth)
# sample.weights <- (1 - dist) * (dist <= 1) / bandwidth

# Estimate a local linear regression with the running variable Z conditional on covariates X = x:
# Y = c(x) + tau(x) W + b(x) Z.
# Specify gradient.weights = c(1, 0) to target heterogeneity in the RDD coefficient tau(x).
# Also, fit forest on subset with non-zero weights for faster estimation.
subset <- sample.weights > 0
lmf <- lm_forest(X[subset, ], Y[subset], cbind(W, Z)[subset, ],
                 sample.weights = sample.weights[subset], gradient.weights = c(1, 0))
tau.hat <- predict(lmf)$predictions[, 1, ]

# Plot estimated tau(x) vs simulated ground truth.
plot(X[subset, 1], tau.hat)
points(X[subset, 1], tau[subset], col = "red", cex = 0.1)
}

Description

Merges a list of forests that were grown using the same data into one large forest.

Usage

merge_forests(forest_list, compute.oob.predictions = TRUE)

Arguments

Value

A single forest containing all the trees in each forest in the input list.

Examples

# Train standard regression forests
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
r.forest1 <- regression_forest(X, Y, compute.oob.predictions = FALSE, num.trees = 100)
r.forest2 <- regression_forest(X, Y, compute.oob.predictions = FALSE, num.trees = 100)

# Join the forests together. The resulting forest will contain 200 trees.
big_rf <- merge_forests(list(r.forest1, r.forest2))

Description

Trains a causal forest that can be used to estimate conditional average treatment effects tau_k(X). When the treatment assignment W is {1, ..., K} and unconfounded, we have tau_k(X) = E[Y(k) - Y(1) | X = x] where Y(k) and Y(1) are potential outcomes corresponding to the treatment state for arm k and the baseline arm 1.

Usage

multi_arm_causal_forest(
  X,
  Y,
  W,
  Y.hat = NULL,
  W.hat = NULL,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  stabilize.splits = TRUE,
  ci.group.size = 2,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Details

This forest fits a multi-arm treatment estimate following the multivariate extension of the "R-learner" suggested in Nie and Wager (2021), with kernel weights derived by the GRF algorithm (Athey, Tibshirani, and Wager, 2019). In particular, with K arms, and W encoded as {0, 1}^(K-1), we estimate, for a target sample x, and a chosen baseline arm:

$\hat \tau(x) = argmin_{\tau} \left\{ \sum_{i=1}^{n} \alpha_i (x) \left( Y_i - \hat m^{(-i)}(X_i) - c(x) - \left\langle W_i - \hat e^{(-i)}(X_i), \, \tau(X_i) \right\rangle \right)^2 \right\}$,

where the angle brackets indicates an inner product, e(X) = E[W | X = x] is a (vector valued) generalized propensity score, and m(x) = E[Y | X = x]. The forest weights alpha(x) are derived from a generalized random forest splitting on the vector-valued gradient of tau(x). (The intercept c(x) is a nuisance parameter not directly estimated). By default, e(X) and m(X) are estimated using two separate random forests, a probability forest and regression forest respectively (optionally provided through the arguments W.hat and Y.hat). The k-th element of tau(x) measures the conditional average treatment effect of the k-th treatment arm at X = x for k = 1, ..., K-1. The treatment effect for multiple outcomes can be estimated jointly (i.e. Y can be vector-valued) - in which case the splitting rule takes into account all outcomes simultaneously (specifically, we concatenate the gradient vector for each outcome).

For a single treatment and outcome, this forest is equivalent to a causal forest, however, they may produce different results due to differences in numerics.

Value

A trained multi arm causal forest object.

References

Athey, Susan, Julie Tibshirani, and Stefan Wager. "Generalized Random Forests". Annals of Statistics, 47(2), 2019.

Nie, Xinkun, and Stefan Wager. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects". Biometrika, 108(2), 2021.

Examples

# Train a multi arm causal forest.
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- as.factor(sample(c("A", "B", "C"), n, replace = TRUE))
Y <- X[, 1] + X[, 2] * (W == "B") - 1.5 * X[, 2] * (W == "C") + rnorm(n)
mc.forest <- multi_arm_causal_forest(X, Y, W)

# Predict contrasts (out-of-bag) using the forest.
# Fitting several outcomes jointly is supported, and the returned prediction array has
# dimension [num.samples, num.contrasts, num.outcomes]. Since num.outcomes is one in
# this example, we use drop = TRUE to ignore this singleton dimension.
mc.pred <- predict(mc.forest, drop = TRUE)

# By default, the first ordinal treatment is used as baseline ("A" in this example),
# giving two contrasts tau_B = Y(B) - Y(A), tau_C = Y(C) - Y(A)
tau.hat <- mc.pred$predictions

plot(X[, 2], tau.hat[, "B - A"], ylab = "tau.contrast")
abline(0, 1, col = "red")
points(X[, 2], tau.hat[, "C - A"], col = "blue")
abline(0, -1.5, col = "red")
legend("topleft", c("B - A", "C - A"), col = c("black", "blue"), pch = 19)

# The average treatment effect of the arms with "A" as baseline.
average_treatment_effect(mc.forest)

# The conditional response surfaces mu_k(X) for a single outcome can be reconstructed from
# the contrasts tau_k(x), the treatment propensities e_k(x), and the conditional mean m(x).
# Given treatment "A" as baseline we have:
# m(x) := E[Y | X] = E[Y(A) | X] + E[W_B (Y(B) - Y(A))] + E[W_C (Y(C) - Y(A))]
# which given unconfoundedness is equal to:
# m(x) = mu(A, x) + e_B(x) tau_B(X) + e_C(x) tau_C(x)
# Rearranging and plugging in the above expressions, we obtain the following estimates
# * mu(A, x) = m(x) - e_B(x) tau_B(x) - e_C(x) tau_C(x)
# * mu(B, x) = m(x) + (1 - e_B(x)) tau_B(x) - e_C(x) tau_C(x)
# * mu(C, x) = m(x) - e_B(x) tau_B(x) + (1 - e_C(x)) tau_C(x)
Y.hat <- mc.forest$Y.hat
W.hat <- mc.forest$W.hat

muA <- Y.hat - W.hat[, "B"] * tau.hat[, "B - A"] - W.hat[, "C"] * tau.hat[, "C - A"]
muB <- Y.hat + (1 - W.hat[, "B"]) * tau.hat[, "B - A"] - W.hat[, "C"] * tau.hat[, "C - A"]
muC <- Y.hat - W.hat[, "B"] * tau.hat[, "B - A"] + (1 - W.hat[, "C"]) * tau.hat[, "C - A"]

# These can also be obtained with some array manipulations.
# (the first column is always the baseline arm)
Y.hat.baseline <- Y.hat - rowSums(W.hat[, -1, drop = FALSE] * tau.hat)
mu.hat.matrix <- cbind(Y.hat.baseline, c(Y.hat.baseline) + tau.hat)
colnames(mu.hat.matrix) <- levels(W)
head(mu.hat.matrix)

# The reference level for contrast prediction can be changed with `relevel`.
# Fit and predict with treatment B as baseline:
W <- relevel(W, ref = "B")
mc.forest.B <- multi_arm_causal_forest(X, Y, W)

Description

Trains a regression forest that can be used to estimate the conditional mean functions mu_i(x) = E[Y_i | X = x]

Usage

multi_regression_forest(
  X,
  Y,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained multi regression forest object.

Examples

# Train a standard regression forest.
n <- 500
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <-  X[, 1, drop = FALSE] %*% cbind(1, 2) + rnorm(n)
mr.forest <- multi_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
mr.pred <- predict(mr.forest, X.test)

# Predict on out-of-bag training samples.
mr.pred <- predict(mr.forest)

Description

The direction NAs are sent are indicated with the arrow fill. An empty arrow indicates that NAs are sent that way. If trained without missing values, both arrows are filled.

Usage

## S3 method for class 'grf_tree'
plot(x, include.na.path = NULL, ...)

Arguments

Examples

## Not run: 
# Plot a tree in the forest (requires the `DiagrammeR` package).
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
c.forest <- causal_forest(X, Y, W)
plot(tree <- get_tree(c.forest, 1))
# Compute the leaf nodes the first five samples falls into.
leaf.nodes <- get_leaf_node(tree, X[1:5, ])

# Saving a plot in .svg can be done with the `DiagrammeRsvg` package.
install.packages("DiagrammeRsvg")
tree.plot = plot(tree)
cat(DiagrammeRsvg::export_svg(tree.plot), file = 'plot.svg')

## End(Not run)

Description

Plot the Targeting Operator Characteristic curve.

Usage

## S3 method for class 'rank_average_treatment_effect'
plot(x, ..., ci.args = list(), abline.args = list(), legend.args = list())

Arguments

Description

Gets estimates of E[Y|X=x] using a trained regression forest.

Usage

## S3 method for class 'boosted_regression_forest'
predict(
  object,
  newdata = NULL,
  boost.predict.steps = NULL,
  num.threads = NULL,
  ...
)

Arguments

Value

A vector of predictions.

Examples

# Train a boosted regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
r.boosted.forest <- boosted_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
r.pred <- predict(r.boosted.forest, X.test)

# Predict on out-of-bag training samples.
r.pred <- predict(r.boosted.forest)

Description

Gets estimates of tau(x) using a trained causal forest.

Usage

## S3 method for class 'causal_forest'
predict(
  object,
  newdata = NULL,
  linear.correction.variables = NULL,
  ll.lambda = NULL,
  ll.weight.penalty = FALSE,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

Vector of predictions, along with estimates of the error and (optionally) its variance estimates. Column 'predictions' contains estimates of the conditional average treatent effect (CATE). The square-root of column 'variance.estimates' is the standard error of CATE. For out-of-bag estimates, we also output the following error measures. First, column 'debiased.error' contains estimates of the 'R-loss' criterion, (See Nie and Wager, 2021 for a justification). Second, column 'excess.error' contains jackknife estimates of the Monte-carlo error (Wager, Hastie, Efron 2014), a measure of how unstable estimates are if we grow forests of the same size on the same data set. The sum of 'debiased.error' and 'excess.error' is the raw error attained by the current forest, and 'debiased.error' alone is an estimate of the error attained by a forest with an infinite number of trees. We recommend that users grow enough forests to make the 'excess.error' negligible.

References

Friedberg, Rina, Julie Tibshirani, Susan Athey, and Stefan Wager. "Local Linear Forests". Journal of Computational and Graphical Statistics, 30(2), 2020.

Wager, Stefan, Trevor Hastie, and Bradley Efron. "Confidence intervals for random forests: The jackknife and the infinitesimal jackknife." The Journal of Machine Learning Research 15(1), 2014.

Nie, Xinkun, and Stefan Wager. "Quasi-Oracle Estimation of Heterogeneous Treatment Effects". Biometrika, 108(2), 2021.

Examples

# Train a causal forest.
n <- 100
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.5)
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
c.forest <- causal_forest(X, Y, W)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
c.pred <- predict(c.forest, X.test)

# Predict on out-of-bag training samples.
c.pred <- predict(c.forest)

# Predict with confidence intervals; growing more trees is now recommended.
c.forest <- causal_forest(X, Y, W, num.trees = 500)
c.pred <- predict(c.forest, X.test, estimate.variance = TRUE)

Description

Gets estimates of tau(X) using a trained causal survival forest.

Usage

## S3 method for class 'causal_survival_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

Vector of predictions along with optional variance estimates.

Examples

# Train a causal survival forest targeting a Restricted Mean Survival Time (RMST)
# with maximum follow-up time set to `horizon`.
n <- 2000
p <- 5
X <- matrix(runif(n * p), n, p)
W <- rbinom(n, 1, 0.5)
horizon <- 1
failure.time <- pmin(rexp(n) * X[, 1] + W, horizon)
censor.time <- 2 * runif(n)
Y <- pmin(failure.time, censor.time)
D <- as.integer(failure.time <= censor.time)
# Save computation time by constraining the event grid by discretizing (rounding) continuous events.
cs.forest <- causal_survival_forest(X, round(Y, 2), W, D, horizon = horizon)
# Or do so more flexibly by defining your own time grid using the failure.times argument.
# grid <- seq(min(Y), max(Y), length.out = 150)
# cs.forest <- causal_survival_forest(X, Y, W, D, horizon = horizon, failure.times = grid)

# Predict using the forest.
X.test <- matrix(0.5, 10, p)
X.test[, 1] <- seq(0, 1, length.out = 10)
cs.pred <- predict(cs.forest, X.test)

# Predict on out-of-bag training samples.
cs.pred <- predict(cs.forest)

# Predict with confidence intervals; growing more trees is now recommended.
c.pred <- predict(cs.forest, X.test, estimate.variance = TRUE)

# Compute a doubly robust estimate of the average treatment effect.
average_treatment_effect(cs.forest)

# Compute the best linear projection on the first covariate.
best_linear_projection(cs.forest, X[, 1])

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

Description

Gets estimates of tau(x) using a trained instrumental forest.

Usage

## S3 method for class 'instrumental_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

Vector of predictions, along with (optional) variance estimates.

Examples

# Train an instrumental forest.
n <- 2000
p <- 5
X <- matrix(rbinom(n * p, 1, 0.5), n, p)
Z <- rbinom(n, 1, 0.5)
Q <- rbinom(n, 1, 0.5)
W <- Q * Z
tau <-  X[, 1] / 2
Y <- rowSums(X[, 1:3]) + tau * W + Q + rnorm(n)
iv.forest <- instrumental_forest(X, Y, W, Z)

# Predict on out-of-bag training samples.
iv.pred <- predict(iv.forest)

# Estimate a (local) average treatment effect.
average_treatment_effect(iv.forest)

Description

Gets estimates of E[Y|X=x] using a trained regression forest.

Usage

## S3 method for class 'll_regression_forest'
predict(
  object,
  newdata = NULL,
  linear.correction.variables = NULL,
  ll.lambda = NULL,
  ll.weight.penalty = FALSE,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

A vector of predictions.

Examples

# Train the forest.
n <- 50
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
forest <- ll_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
predictions <- predict(forest, X.test)

# Predict on out-of-bag training samples.
predictions.oob <- predict(forest)

Description

Gets estimates of $h_k(x)$, k = 1..K in the conditionally linear model $Y = c(x) + h_1(x)W_1 + ... + h_K(x)W_K$, for a target sample X = x.

Usage

## S3 method for class 'lm_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  drop = FALSE,
  ...
)

Arguments

Value

A list with elements 'predictions': a 3d array of dimension [num.samples, K, M] with predictions for regressor W, for each outcome 1,..,M (singleton dimensions in this array can be dropped by passing the 'drop' argument to '[', or with the shorthand '$predictions[,,]'), and optionally 'variance.estimates': a matrix with K columns with variance estimates.

Examples

if (require("rdd", quietly = TRUE)) {
# Train a LM Forest to estimate CATEs in a regression discontinuity design.
# Simulate a simple example with a heterogeneous jump in the CEF.
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
Z <- runif(n, -4, 4)
cutoff <- 0
W <- as.numeric(Z >= cutoff)
tau <- pmax(0.5 * X[, 1], 0)
Y <- tau * W  + 1 / (1 + exp(2 * Z)) + 0.2 * rnorm(n)

# Compute the Imbens-Kalyanaraman MSE-optimal bandwidth for a local linear regression.
bandwidth <- IKbandwidth(Z, Y, cutoff)
# Compute kernel weights for a triangular kernel.
sample.weights <- kernelwts(Z, cutoff, bandwidth, "triangular")

# Alternatively, specify bandwith and triangular kernel weights without using the `rdd` package.
# bandwidth <- # user can hand-specify this.
# dist <- abs((Z - cutoff) / bandwidth)
# sample.weights <- (1 - dist) * (dist <= 1) / bandwidth

# Estimate a local linear regression with the running variable Z conditional on covariates X = x:
# Y = c(x) + tau(x) W + b(x) Z.
# Specify gradient.weights = c(1, 0) to target heterogeneity in the RDD coefficient tau(x).
# Also, fit forest on subset with non-zero weights for faster estimation.
subset <- sample.weights > 0
lmf <- lm_forest(X[subset, ], Y[subset], cbind(W, Z)[subset, ],
                 sample.weights = sample.weights[subset], gradient.weights = c(1, 0))
tau.hat <- predict(lmf)$predictions[, 1, ]

# Plot estimated tau(x) vs simulated ground truth.
plot(X[subset, 1], tau.hat)
points(X[subset, 1], tau[subset], col = "red", cex = 0.1)
}

Description

Gets estimates of contrasts tau_k(x) using a trained multi arm causal forest (k = 1,...,K-1 where K is the number of treatments).

Usage

## S3 method for class 'multi_arm_causal_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  drop = FALSE,
  ...
)

Arguments

Value

A list with elements 'predictions': a 3d array of dimension [num.samples, K-1, M] with predictions for each contrast, for each outcome 1,..,M (singleton dimensions in this array can be dropped by passing the 'drop' argument to '[', or with the shorthand '$predictions[,,]'), and optionally 'variance.estimates': a matrix with K-1 columns with variance estimates for each contrast.

Examples

# Train a multi arm causal forest.
n <- 500
p <- 10
X <- matrix(rnorm(n * p), n, p)
W <- as.factor(sample(c("A", "B", "C"), n, replace = TRUE))
Y <- X[, 1] + X[, 2] * (W == "B") - 1.5 * X[, 2] * (W == "C") + rnorm(n)
mc.forest <- multi_arm_causal_forest(X, Y, W)

# Predict contrasts (out-of-bag) using the forest.
# Fitting several outcomes jointly is supported, and the returned prediction array has
# dimension [num.samples, num.contrasts, num.outcomes]. Since num.outcomes is one in
# this example, we use drop = TRUE to ignore this singleton dimension.
mc.pred <- predict(mc.forest, drop = TRUE)

# By default, the first ordinal treatment is used as baseline ("A" in this example),
# giving two contrasts tau_B = Y(B) - Y(A), tau_C = Y(C) - Y(A)
tau.hat <- mc.pred$predictions

plot(X[, 2], tau.hat[, "B - A"], ylab = "tau.contrast")
abline(0, 1, col = "red")
points(X[, 2], tau.hat[, "C - A"], col = "blue")
abline(0, -1.5, col = "red")
legend("topleft", c("B - A", "C - A"), col = c("black", "blue"), pch = 19)

# The average treatment effect of the arms with "A" as baseline.
average_treatment_effect(mc.forest)

# The conditional response surfaces mu_k(X) for a single outcome can be reconstructed from
# the contrasts tau_k(x), the treatment propensities e_k(x), and the conditional mean m(x).
# Given treatment "A" as baseline we have:
# m(x) := E[Y | X] = E[Y(A) | X] + E[W_B (Y(B) - Y(A))] + E[W_C (Y(C) - Y(A))]
# which given unconfoundedness is equal to:
# m(x) = mu(A, x) + e_B(x) tau_B(X) + e_C(x) tau_C(x)
# Rearranging and plugging in the above expressions, we obtain the following estimates
# * mu(A, x) = m(x) - e_B(x) tau_B(x) - e_C(x) tau_C(x)
# * mu(B, x) = m(x) + (1 - e_B(x)) tau_B(x) - e_C(x) tau_C(x)
# * mu(C, x) = m(x) - e_B(x) tau_B(x) + (1 - e_C(x)) tau_C(x)
Y.hat <- mc.forest$Y.hat
W.hat <- mc.forest$W.hat

muA <- Y.hat - W.hat[, "B"] * tau.hat[, "B - A"] - W.hat[, "C"] * tau.hat[, "C - A"]
muB <- Y.hat + (1 - W.hat[, "B"]) * tau.hat[, "B - A"] - W.hat[, "C"] * tau.hat[, "C - A"]
muC <- Y.hat - W.hat[, "B"] * tau.hat[, "B - A"] + (1 - W.hat[, "C"]) * tau.hat[, "C - A"]

# These can also be obtained with some array manipulations.
# (the first column is always the baseline arm)
Y.hat.baseline <- Y.hat - rowSums(W.hat[, -1, drop = FALSE] * tau.hat)
mu.hat.matrix <- cbind(Y.hat.baseline, c(Y.hat.baseline) + tau.hat)
colnames(mu.hat.matrix) <- levels(W)
head(mu.hat.matrix)

# The reference level for contrast prediction can be changed with `relevel`.
# Fit and predict with treatment B as baseline:
W <- relevel(W, ref = "B")
mc.forest.B <- multi_arm_causal_forest(X, Y, W)

Description

Gets estimates of E[Y_i | X = x] using a trained multi regression forest.

Usage

## S3 method for class 'multi_regression_forest'
predict(object, newdata = NULL, num.threads = NULL, drop = FALSE, ...)

Arguments

Value

A list containing 'predictions': a matrix of predictions for each outcome.

Examples

# Train a standard regression forest.
n <- 500
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <-  X[, 1, drop = FALSE] %*% cbind(1, 2) + rnorm(n)
mr.forest <- multi_regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
mr.pred <- predict(mr.forest, X.test)

# Predict on out-of-bag training samples.
mr.pred <- predict(mr.forest)

Description

Gets estimates of P[Y = k | X = x] using a trained forest.

Usage

## S3 method for class 'probability_forest'
predict(
  object,
  newdata = NULL,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

A list with attributes 'predictions': a matrix of predictions for each class, and optionally the attribute 'variance.estimates': a matrix of variance estimates for each class.

Examples

# Train a probability forest.
p <- 5
n <- 2000
X <- matrix(rnorm(n*p), n, p)
prob <- 1 / (1 + exp(-X[, 1] - X[, 2]))
Y <- as.factor(rbinom(n, 1, prob))
p.forest <- probability_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 10, p)
X.test[, 1] <- seq(-1.5, 1.5, length.out = 10)
p.hat <- predict(p.forest, X.test, estimate.variance = TRUE)

# Plot the estimated success probabilities with 95 % confidence bands.
prob.test <- 1 / (1 + exp(-X.test[, 1] - X.test[, 2]))
p.true <- cbind(`0` = 1 - prob.test, `1` = prob.test)
plot(X.test[, 1], p.true[, "1"], col = 'red', ylim = c(0, 1))
points(X.test[, 1], p.hat$predictions[, "1"], pch = 16)
lines(X.test[, 1], (p.hat$predictions + 2 * sqrt(p.hat$variance.estimates))[, "1"])
lines(X.test[, 1], (p.hat$predictions - 2 * sqrt(p.hat$variance.estimates))[, "1"])

# Predict on out-of-bag training samples.
p.hat <- predict(p.forest)

Description

Gets estimates of the conditional quantiles of Y given X using a trained forest.

Usage

## S3 method for class 'quantile_forest'
predict(object, newdata = NULL, quantiles = NULL, num.threads = NULL, ...)

Arguments

Value

A list with elements 'predictions': a matrix with predictions at each test point for each desired quantile.

Examples

# Train a quantile forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
q.forest <- quantile_forest(X, Y, quantiles = c(0.1, 0.5, 0.9))

# Predict on out-of-bag training samples.
q.pred <- predict(q.forest)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
q.pred <- predict(q.forest, X.test)

Description

Gets estimates of E[Y|X=x] using a trained regression forest.

Usage

## S3 method for class 'regression_forest'
predict(
  object,
  newdata = NULL,
  linear.correction.variables = NULL,
  ll.lambda = NULL,
  ll.weight.penalty = FALSE,
  num.threads = NULL,
  estimate.variance = FALSE,
  ...
)

Arguments

Value

Vector of predictions, along with estimates of the error and (optionally) its variance estimates. Column 'predictions' contains estimates of E[Y|X=x]. The square-root of column 'variance.estimates' is the standard error the test mean-squared error. Column 'excess.error' contains jackknife estimates of the Monte-carlo error. The sum of 'debiased.error' and 'excess.error' is the raw error attained by the current forest, and 'debiased.error' alone is an estimate of the error attained by a forest with an infinite number of trees. We recommend that users grow enough forests to make the 'excess.error' negligible.

Examples

# Train a standard regression forest.
n <- 50
p <- 10
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] * rnorm(n)
r.forest <- regression_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 101, p)
X.test[, 1] <- seq(-2, 2, length.out = 101)
r.pred <- predict(r.forest, X.test)

# Predict on out-of-bag training samples.
r.pred <- predict(r.forest)

# Predict with confidence intervals; growing more trees is now recommended.
r.forest <- regression_forest(X, Y, num.trees = 100)
r.pred <- predict(r.forest, X.test, estimate.variance = TRUE)

Description

Gets estimates of the conditional survival function S(t, x) = P[T > t | X = x] using a trained survival forest. The curve can be estimated by Kaplan-Meier, or Nelson-Aalen.

Usage

## S3 method for class 'survival_forest'
predict(
  object,
  newdata = NULL,
  failure.times = NULL,
  prediction.times = c("curve", "time"),
  prediction.type = c("Kaplan-Meier", "Nelson-Aalen"),
  num.threads = NULL,
  ...
)

Arguments

Value

A list with elements

• predictions: a matrix of survival curves. If prediction.times = "curve" then each row is the survival curve for sample Xi: predictions[i, j] = S(failure.times[j], Xi). If prediction.times = "time" then each row is the survival curve at time point failure.times[i] for sample Xi: predictions[i, ] = S(failure.times[i], Xi).

• failure.times: a vector of event times t for the survival curve.

Examples

# Train a standard survival forest.
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
failure.time <- exp(0.5 * X[, 1]) * rexp(n)
censor.time <- 2 * rexp(n)
Y <- pmin(failure.time, censor.time)
D <- as.integer(failure.time <= censor.time)
# Save computation time by constraining the event grid by discretizing (rounding) continuous events.
s.forest <- survival_forest(X, round(Y, 2), D)
# Or do so more flexibly by defining your own time grid using the failure.times argument.
# grid <- seq(min(Y[D==1]), max(Y[D==1]), length.out = 150)
# s.forest <- survival_forest(X, Y, D, failure.times = grid)

# Predict using the forest.
X.test <- matrix(0, 3, p)
X.test[, 1] <- seq(-2, 2, length.out = 3)
s.pred <- predict(s.forest, X.test)

# Plot the survival curve.
plot(NA, NA, xlab = "failure time", ylab = "survival function",
     xlim = range(s.pred$failure.times),
     ylim = c(0, 1))
for(i in 1:3) {
  lines(s.pred$failure.times, s.pred$predictions[i,], col = i)
  s.true = exp(-s.pred$failure.times / exp(0.5 * X.test[i, 1]))
  lines(s.pred$failure.times, s.true, col = i, lty = 2)
}

# Predict on out-of-bag training samples.
s.pred <- predict(s.forest)

# Compute OOB concordance based on the mortality score in Ishwaran et al. (2008).
s.pred.nelson.aalen <- predict(s.forest, prediction.type = "Nelson-Aalen")
chf.score <- rowSums(-log(s.pred.nelson.aalen$predictions))
if (require("survival", quietly = TRUE)) {
 concordance(Surv(Y, D) ~ chf.score, reverse = TRUE)
}

Description

Print a boosted regression forest

Usage

## S3 method for class 'boosted_regression_forest'
print(x, ...)

Arguments

Description

Print a GRF forest object.

Usage

## S3 method for class 'grf'
print(x, decay.exponent = 2, max.depth = 4, ...)

Arguments

Description

Print a GRF tree object.

Usage

## S3 method for class 'grf_tree'
print(x, ...)

Arguments

Description

Print the Rank-Weighted Average Treatment Effect (RATE).

Usage

## S3 method for class 'rank_average_treatment_effect'
print(x, ...)

Arguments

Description

Print tuning output. Displays average error for q-quantiles of tuned parameters.

Usage

## S3 method for class 'tuning_output'
print(x, tuning.quantiles = seq(0, 1, 0.2), ...)

Arguments

Description

Trains a probability forest that can be used to estimate the conditional class probabilities P[Y = k | X = x]

Usage

probability_forest(
  X,
  Y,
  num.trees = 2000,
  sample.weights = NULL,
  clusters = NULL,
  equalize.cluster.weights = FALSE,
  sample.fraction = 0.5,
  mtry = min(ceiling(sqrt(ncol(X)) + 20), ncol(X)),
  min.node.size = 5,
  honesty = TRUE,
  honesty.fraction = 0.5,
  honesty.prune.leaves = TRUE,
  alpha = 0.05,
  imbalance.penalty = 0,
  ci.group.size = 2,
  compute.oob.predictions = TRUE,
  num.threads = NULL,
  seed = runif(1, 0, .Machine$integer.max)
)

Arguments

Value

A trained probability forest object.

Examples

# Train a probability forest.
p <- 5
n <- 2000
X <- matrix(rnorm(n*p), n, p)
prob <- 1 / (1 + exp(-X[, 1] - X[, 2]))
Y <- as.factor(rbinom(n, 1, prob))
p.forest <- probability_forest(X, Y)

# Predict using the forest.
X.test <- matrix(0, 10, p)
X.test[, 1] <- seq(-1.5, 1.5, length.out = 10)
p.hat <- predict(p.forest, X.test, estimate.variance = TRUE)

# Plot the estimated success probabilities with 95 % confidence bands.
prob.test <- 1 / (1 + exp(-X.test[, 1] - X.test[, 2]))
p.true <- cbind(`0` = 1 - prob.test, `1` = prob.test)
plot(X.test[, 1], p.true[, "1"], col = 'red', ylim = c(0, 1))
points(X.test[, 1], p.hat$predictions[, "1"], pch = 16)
lines(X.test[, 1], (p.hat$predictions + 2 * sqrt(p.hat$variance.estimates))[, "1"])
lines(X.test[, 1], (p.hat$predictions - 2 * sqrt(p.hat$variance.estimates))[, "1"])

# Predict on out-of-bag training samples.
p.hat <- predict(p.forest)