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),
...
)
```

## 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

- ...
Additional arguments passed to

`caret::train`