Task object for meta-learners in causal data structures.

Constructs a hte3_Task object for meta-learners in causal data structures, containing relevant data and nuisance function estimators.

Format

R6Class object.

Value

An object of class hte3_Task.

Details

Class for Storing Data, NPSEM, and Nuisance Function Estimators for hte3 Learners

This class inherits from sl3_Task and tmle3_Task. In addition to all the methods supported by sl3_Task and tmle3_Task, it supports the following functionalities specific to the heterogeneous treatment effect estimation (hte3) framework.

Super classes

sl3::sl3_Task -> tmle3::tmle3_Task -> hte3_Task

Methods

Public methods

• hte3_Task$new()

• hte3_Task$add_nuisance_estimator()

• hte3_Task$get_nuisance_estimates()

• hte3_Task$next_in_chain()

• hte3_Task$subset_task()

• hte3_Task$clone()

Inherited methods

• sl3::sl3_Task$add_columns()
• sl3::sl3_Task$add_interactions()
• sl3::sl3_Task$get_data()
• sl3::sl3_Task$get_folds()
• sl3::sl3_Task$get_node()
• sl3::sl3_Task$has_node()
• sl3::sl3_Task$offset_transformed()
• sl3::sl3_Task$revere_fold_task()
• tmle3::tmle3_Task$generate_counterfactual_task()
• tmle3::tmle3_Task$get_node_bounds()
• tmle3::tmle3_Task$get_regression_task()
• tmle3::tmle3_Task$get_tmle_node()
• tmle3::tmle3_Task$print()
• tmle3::tmle3_Task$scale()
• tmle3::tmle3_Task$unscale()

---

Method new()

Usage

hte3_Task$new(data, npsem, likelihood = NULL, ...)

Arguments

data

A named data frame or data.table containing treatment effect modifiers, potential confounders, treatment, outcome, and optionally weights and subject IDs. See the sl3_Task documentation for further options.

npsem

A list containing tmle3_Node objects containing the non-parametric structural equation model (NPSEM) specifying the relationship between variables in the treatment effect estimation framework.

likelihood

An Likelihood object specifying the relevant likelihood/nuisance estimators for the meta-learner. used for estimating the parameters of the NPSEM.

...

Additional arguments to pass to the initialization function.

---

Method add_nuisance_estimator()

Usage

hte3_Task$add_nuisance_estimator(node, learner)

---

Method get_nuisance_estimates()

Usage

hte3_Task$get_nuisance_estimates(
  nodes,
  hte3_task = NULL,
  fold_number = "validation"
)

---

Method next_in_chain()

Usage

hte3_Task$next_in_chain(
  covariates = NULL,
  outcome = NULL,
  id = NULL,
  weights = NULL,
  offset = NULL,
  time = NULL,
  folds = NULL,
  column_names = NULL,
  new_nodes = NULL,
  new_outcome_type = NULL,
  ...
)

Arguments

...

Additional arguments to pass to the initialization function.

---

Method subset_task()

Usage

hte3_Task$subset_task(row_index, drop_folds = FALSE)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

hte3_Task$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.