vimp_se: Estimate variable importance standard errors

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

Estimate variable importance standard errors

Description

Compute standard error estimates for estimates of variable importance.

Usage

vimp_se(
  eif_full,
  eif_reduced,
  cross_fit = TRUE,
  sample_split = TRUE,
  na.rm = FALSE
)

Arguments

eif_full	the estimated efficient influence function (EIF) based on the full set of covariates.
eif_reduced	the estimated EIF based on the reduced set of covariates.
cross_fit	logical; was cross-fitting used to compute the EIFs? (defaults to TRUE)
sample_split	logical; was sample-splitting used? (defaults to TRUE)
na.rm	logical; should NA's be removed in computation? (defaults to FALSE).

Details

See the paper by Williamson, Gilbert, Simon, and Carone for more details on the mathematics behind this function and the definition of the parameter of interest.

Value

The standard error for the estimated variable importance for the given group of left-out covariates.

bdwilliamson/npvi documentation built on Feb. 1, 2024, 10:46 p.m.