Cross-Validate CRR Models — cross_validate

Cross-validates a collection of CRR learners using the CRR selector loss.

Usage

cross_validate_crr(
  hte_learners,
  hte3_task,
  cv_control = NULL,
  treatment_level = NULL,
  control_level = NULL,
  ...
)

Arguments

hte_learners: A single Lrnr_hte learner, a list of Lrnr_hte learners, or a Stack of Lrnr_hte learners to cross-validate.
hte3_task: An hte3_Task object containing the data and necessary information for heterogeneous treatment effect estimation.
cv_control: A list of control parameters for cross-validation passed to Lrnr_sl. Default is NULL.
treatment_level: The treatment level to be considered the treated group in the contrast used for selection.
control_level: The treatment level to be considered the control group in the contrast used for selection.
...: Additional arguments to pass to the loss function and other functions.

Value

A list containing the following elements:

lrnr_sl: A Lrnr_sl object that represents the cross-validated ensemble of CRR learners.
cv_risk: The cross-validation risk associated with the ensemble, which serves as a measure of the ensemble's predictive performance.
coefficients: The coefficients derived from the cross-validation process, providing insights into the relative importance of different learners within the ensemble.
selection_summary: A compact summary of candidate labels, selected candidate, selected method, and any EP basis-size metadata.

Examples

if (FALSE) { # \dontrun{
library(sl3)

data <- hte3_example_data(n = 80, seed = 1)
task <- hte_task(
  data = data,
  modifiers = c("W1", "W2"),
  confounders = c("W1", "W2", "W3"),
  treatment = "A",
  outcome = "Y_binary",
  propensity_learner = Lrnr_mean$new(),
  outcome_learner = Lrnr_mean$new(),
  mean_learner = Lrnr_mean$new(),
  cross_fit = FALSE
)

cv_fit <- cross_validate_crr(
  list(
    Lrnr_crr_IPW$new(base_learner = Lrnr_mean$new()),
    Lrnr_crr_EP$new(base_learner = Lrnr_mean$new(), sieve_num_basis = 4)
  ),
  task,
  cv_control = list(V = 2)
)

cv_fit$selection_summary
} # }