estimate_nuisances  R Documentation 
Estimate nuisance functions for average valuebased VIMs
estimate_nuisances( fit, X, exposure_name, V = 1, SL.library, sample_splitting, sample_splitting_folds, verbose, weights, cross_fitted_se, split = 1, ... )
fit 
the fitted nuisance function estimator 
X 
the covariates. If 
exposure_name 
(only used if 
V 
the number of folds for crossfitting, defaults to 5. If

SL.library 
a character vector of learners to pass to

sample_splitting 
should we use samplesplitting to estimate the full and
reduced predictiveness? Defaults to 
sample_splitting_folds 
the folds used for samplesplitting;
these identify the observations that should be used to evaluate
predictiveness based on the full and reduced sets of covariates, respectively.
Only used if 
verbose 
should we print progress? defaults to FALSE 
weights 
weights to pass to estimation procedure 
cross_fitted_se 
should we use crossfitting to estimate the standard
errors ( 
split 
the sample split to use 
... 
other arguments to the estimation tool, see "See also". 
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.
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