Estimate the average value under the optimal treatment rule

Compute nonparametric estimate of the average value under the optimal treatment rule.

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

Arguments

nuisance_estimators: 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.
y: the observed outcome (may be within a specified fold, for cross-fitted estimates).
a: the observed treatment assignment (may be within a specified fold, for cross-fitted estimates).
full_y: the observed outcome (not used, defaults to NULL).
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 (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

A named list of: (1) the estimated classification accuracy of the fitted regression function; (2) the estimated influence function; and (3) the IPC EIF predictions.