predictiveness_measure: Construct a Predictiveness Measure

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

Construct a Predictiveness Measure

Description

Construct a Predictiveness Measure

Usage

predictiveness_measure(
  type = character(),
  y = numeric(),
  a = numeric(),
  fitted_values = numeric(),
  cross_fitting_folds = rep(1, length(fitted_values)),
  full_y = NULL,
  nuisance_estimators = list(),
  C = rep(1, length(y)),
  Z = NULL,
  folds_Z = cross_fitting_folds,
  ipc_weights = rep(1, length(y)),
  ipc_fit_type = "SL",
  ipc_eif_preds = numeric(),
  ipc_est_type = "aipw",
  scale = "identity",
  na.rm = TRUE,
  ...
)

Arguments

type	the measure of interest (e.g., "accuracy", "auc", "r_squared")
y	the outcome of interest
a	the exposure of interest (only used if type = "average_value")
fitted_values	fitted values from a regression function using the observed data (may be within a specified fold, for cross-fitted estimates).
cross_fitting_folds	folds for cross-fitting, if used to obtain the fitted values. If not used, a vector of ones.
full_y	the observed outcome (not used, defaults to NULL).
nuisance_estimators	a list of nuisance function estimators on the observed data (may be within a specified fold, for cross-fitted estimates). For the average value measure: an estimator of the optimal treatment rule (f_n); an estimator of the propensity score under the estimated optimal treatment rule (g_n); and an estimator of the outcome regression when treatment is assigned according to the estimated optimal rule (q_n).
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.
folds_Z	either the cross-validation folds for the observed data (no coarsening) or a vector of folds for the fully observed data Z.
ipc_weights	weights for inverse probability of coarsening (IPC) (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 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.
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 NAs be removed in computation? (defaults to FALSE)
...	other arguments to SuperLearner, if ipc_fit_type = "SL".

Value

An object of class "predictiveness_measure", with the following attributes:

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