estimate_eif_projection: Estimate projection of EIF on fully-observed variables

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

Estimate projection of EIF on fully-observed variables

Description

Estimate projection of EIF on fully-observed variables

Usage

estimate_eif_projection(
  obs_grad = NULL,
  C = NULL,
  Z = NULL,
  ipc_fit_type = NULL,
  ipc_eif_preds = NULL,
  ...
)

Arguments

obs_grad	the estimated (observed) EIF
C	the indicator of coarsening (1 denotes observed, 0 denotes unobserved).
Z	either NULL (if no coarsening) or a matrix-like object containing the fully observed data.
ipc_fit_type	if "external", then use ipc_eif_preds; if "SL", fit a SuperLearner to determine the IPC correction to the efficient influence function.
ipc_eif_preds	if ipc_fit_type = "external", the fitted values from a regression of the full-data EIF on the fully observed covariates/outcome; otherwise, not used.
...	other arguments to SuperLearner, if ipc_fit_type = "SL".

Value

the projection of the EIF onto the fully-observed variables

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