estimate_nuisances: Estimate nuisance functions for average value-based VIMs

In bdwilliamson/nova: Perform Inference on Algorithm-Agnostic Variable Importance

Estimate nuisance functions for average value-based VIMs

Description

Estimate nuisance functions for average value-based VIMs

Usage

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.

bdwilliamson/nova documentation built on Feb. 1, 2024, 10:04 p.m.