evalHTE (version 0.1.1)

estimate_hte: Evaluate Heterogeneous Treatment Effects

Description

Evaluate Heterogeneous Treatment Effects

Usage

estimate_hte(
  treatment,
  form,
  data,
  algorithms,
  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,
  meta_learner = "slearner",
  ...
)

Value

An object of hte class

Arguments

treatment

Treatment variable

form

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

data

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

algorithms

List of machine learning algorithms to be used.

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 estimate heterogeneous treatment effects.

SL_library

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

meta_learner

The type of meta-learner to use (e.g., "slearner", "tlearner"). Default is "slearner".

...

Additional arguments passed to caret::train.