Estimate nuisance functions for average value-based VIMs

estimate_nuisances(
  fit,
  X,
  exposure_name,
  V = 1,
  SL.library,
  sample_splitting,
  sample_splitting_folds,
  verbose,
  weights,
  cross_fitted_se,
  split = 1,
  ...
)

Arguments

fit: the fitted nuisance function estimator
X: the covariates. If type = "average_value", then the exposure variable should be part of X, with its name provided in exposure_name.
exposure_name: (only used if type = "average_value") the name of the exposure of interest; binary, with 1 indicating presence of the exposure and 0 indicating absence of the exposure.
V: the number of folds for cross-fitting, defaults to 5. If sample_splitting = TRUE, then a special type of V-fold cross-fitting is done. See Details for a more detailed explanation.
SL.library: a character vector of learners to pass to SuperLearner, if f1 and f2 are Y and X, respectively. Defaults to SL.glmnet, SL.xgboost, and SL.mean.
sample_splitting: should we use sample-splitting to estimate the full and reduced predictiveness? Defaults to TRUE, since inferences made using sample_splitting = FALSE will be invalid for variables with truly zero importance.
sample_splitting_folds: the folds used for sample-splitting; these identify the observations that should be used to evaluate predictiveness based on the full and reduced sets of covariates, respectively. Only used if run_regression = FALSE.
verbose: should we print progress? defaults to FALSE
weights: weights to pass to estimation procedure
cross_fitted_se: should we use cross-fitting to estimate the standard errors (TRUE, the default) or not (FALSE)?
split: the sample split to use
...: other arguments to the estimation tool, see "See also".

Value

nuisance function estimators for use in the average value VIM: the treatment assignment based on the estimated optimal rule (based on the estimated outcome regression); the expected outcome under the estimated optimal rule; and the estimated propensity score.