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