Lrnr_crr_EP Class

This class defines an efficient plug-in (EP) meta-learner for the conditional relative average treatment effect (crr).

Format

An R6 class with public methods to initialize the learner, create a regression task, and access the base learner.

Super classes

sl3::Lrnr_base -> hte3::Lrnr_hte -> Lrnr_crr_EP

Methods

Public methods

• Lrnr_crr_EP$new()

• Lrnr_crr_EP$get_pseudo_data()

• Lrnr_crr_EP$clone()

Inherited methods

• sl3::Lrnr_base$assert_trained()
• sl3::Lrnr_base$base_chain()
• sl3::Lrnr_base$base_predict()
• sl3::Lrnr_base$base_train()
• sl3::Lrnr_base$chain()
• sl3::Lrnr_base$custom_chain()
• sl3::Lrnr_base$get_outcome_range()
• sl3::Lrnr_base$get_outcome_type()
• sl3::Lrnr_base$predict()
• sl3::Lrnr_base$predict_fold()
• sl3::Lrnr_base$print()
• sl3::Lrnr_base$process_formula()
• sl3::Lrnr_base$reparameterize()
• sl3::Lrnr_base$retrain()
• sl3::Lrnr_base$sample()
• sl3::Lrnr_base$set_train()
• sl3::Lrnr_base$subset_covariates()
• sl3::Lrnr_base$train()
• sl3::Lrnr_base$train_sublearners()
• hte3::Lrnr_hte$check_treatment_type()
• hte3::Lrnr_hte$get_modifiers()
• hte3::Lrnr_hte$make_metalearner_task()

---

Method new()

Usage

Lrnr_crr_EP$new(
  base_learner,
  sieve_num_basis = NULL,
  sieve_interaction_order = 3,
  treatment_level = NULL,
  control_level = NULL,
  ...
)

Arguments

base_learner

A sl3 learner object inheriting from Lrnr_base that specifies the base supervised learning algorithm used by the meta-learner.

sieve_num_basis

The number of trigonometric basis functions used to construct the CRR EP sieve space through sieve_preprocess. Use sieve_num_basis for one fixed EP fit and sieve_basis_grid in fit_crr() for wrapper-level EP basis-size selection.

sieve_interaction_order

The maximum interaction degree of tensor-product basis functions in the EP sieve basis.

treatment_level

Optional treated level used for the contrast.

control_level

Optional control level used for the contrast.

---

Method get_pseudo_data()

Usage

Lrnr_crr_EP$get_pseudo_data(
  hte3_task,
  treatment_level = NULL,
  control_level = NULL,
  train = TRUE,
  ...
)

Arguments

treatment_level

Optional treated level used for the contrast.

control_level

Optional control level used for the contrast.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Lrnr_crr_EP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.