est_predictiveness: Estimate a nonparametric predictiveness functional

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

Estimate a nonparametric predictiveness functional

Description

Compute nonparametric estimates of the chosen measure of predictiveness.

Usage

est_predictiveness(
  fitted_values,
  y,
  a = NULL,
  full_y = NULL,
  type = "r_squared",
  C = rep(1, length(y)),
  Z = NULL,
  ipc_weights = rep(1, length(C)),
  ipc_fit_type = "external",
  ipc_eif_preds = rep(1, length(C)),
  ipc_est_type = "aipw",
  scale = "identity",
  na.rm = FALSE,
  nuisance_estimators = NULL,
  ...
)

Arguments

fitted_values	fitted values from a regression function using the observed data.
y	the observed outcome.
a	the observed treatment assignment (may be within a specified fold, for cross-fitted estimates). Only used if type = "average_value".
full_y	the observed outcome (from the entire dataset, for cross-fitted estimates).
type	which parameter are you estimating (defaults to r_squared, for R-squared-based variable importance)?
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_weights	weights for inverse probability of coarsening (e.g., inverse weights from a two-phase sample) weighted estimation. Assumed to be already inverted (i.e., ipc_weights = 1 / [estimated probability weights]).
ipc_fit_type	if "external", then use ipc_eif_preds; if "SL", fit a SuperLearner to determine the 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.
ipc_est_type	IPC correction, either "ipw" (for classical inverse probability weighting) or "aipw" (for augmented inverse probability weighting; the default).
scale	if doing an IPC correction, then the scale that the correction should be computed on (e.g., "identity"; or "logit" to logit-transform, apply the correction, and back-transform).
na.rm	logical; should NA's be removed in computation? (defaults to FALSE)
nuisance_estimators	(only used if type = "average_value") a list of nuisance function estimators on the observed data (may be within a specified fold, for cross-fitted estimates). Specifically: an estimator of the optimal treatment rule; an estimator of the propensity score under the estimated optimal treatment rule; and an estimator of the outcome regression when treatment is assigned according to the estimated optimal rule.
...	other arguments to SuperLearner, if ipc_fit_type = "SL".

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

A list, with: the estimated predictiveness; the estimated efficient influence function; and the predictions of the EIF based on inverse probability of censoring.

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