estimate_itr {evalITR}

Estimate individual treatment rules (ITR)

Description

Estimate individual treatment rules (ITR)

Usage

estimate_itr(
  treatment,
  form,
  data,
  algorithms,
  budget,
  n_folds = 5,
  split_ratio = 0,
  ngates = 5,
  preProcess = NULL,
  weights = NULL,
  trControl = caret::trainControl(method = "none"),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3),
  user_model = NULL,
  SL_library = NULL,
  ...
)

Arguments

treatment

Treatment variable

form

a formula object that takes the form y ~ T + x1 + x2 + ....

data

A data frame that contains the outcome y and the treatment T.

algorithms

List of machine learning algorithms to be used.

budget

The maximum percentage of population that can be treated under the budget constraint.

n_folds

Number of cross-validation folds. Default is 5.

split_ratio

Split ratio between train and test set under sample splitting. Default is 0.

ngates

The number of groups to separate the data into. The groups are determined by tau. Default is 5.

preProcess

caret parameter

weights

caret parameter

trControl

caret parameter

tuneGrid

caret parameter

tuneLength

caret parameter

user_model

A user-defined function to create an ITR. The function should take the data as input and return a model to estimate the ITR.

SL_library

A list of machine learning algorithms to be used in the super learner.

...

Additional arguments passed to caret::train

Value

An object of itr class