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,
...
)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.
the fitted nuisance function estimator
the covariates. If type = "average_value", then the exposure
variable should be part of X, with its name provided in 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.
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.
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.
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.
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.
should we print progress? defaults to FALSE
weights to pass to estimation procedure
should we use cross-fitting to estimate the standard
errors (TRUE, the default) or not (FALSE)?
the sample split to use
other arguments to the estimation tool, see "See also".