Predict with a causal survival forest forest

Source: R/causal_survival_forest.R

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

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

Arguments

object

The trained forest.
newdata

Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.
num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.
estimate.variance

Whether variance estimates for hattau(x) are desired (for confidence intervals).
...

Additional arguments (currently ignored).

Value

Vector of predictions along with optional variance estimates.

Examples

# \donttest{
# Train a causal survival forest targeting a Restricted Mean Survival Time (RMST)
# with maxium 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)
# Discretizing continuous events decreases runtime.
Y <- round(pmin(failure.time, censor.time), 2)
D <- as.integer(failure.time <= censor.time)
cs.forest <- causal_survival_forest(X, Y, W, D, horizon = horizon)

# 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)

# Compute a doubly robust estimate of the average treatment effect.
average_treatment_effect(cs.forest)
#>   estimate    std.err 
#> 0.58237959 0.01134973 

# Compute the best linear projection on the first covariate.
best_linear_projection(cs.forest, X[, 1])
#> 
#> Best linear projection of the conditional average treatment effect.
#> Confidence intervals are cluster- and heteroskedasticity-robust (HC3):
#> 
#>              Estimate Std. Error t value  Pr(>|t|)    
#> (Intercept)  0.915797   0.015725  58.238 < 2.2e-16 ***
#> A1          -0.655009   0.034126 -19.194 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 

# Train a causal survival forest targeting an absolute risk difference
# at the median timepoint `horizon`.
cs.forest.prob <- causal_survival_forest(X, Y, W, D, target = "survival.probability", horizon = 0.5)
# }