plateau_selector: Agnostic IPW Estimator Selector via Lepski's and...

View source: R/selector_plateau.R

plateau_selectorR Documentation

Agnostic IPW Estimator Selector via Lepski's and Variance-Blind Methods

Description

Agnostic IPW Estimator Selector via Lepski's and Variance-Blind Methods

Usage

plateau_selector(
  W,
  A,
  Y,
  delta = 0,
  gn_pred_natural,
  gn_pred_shifted,
  gn_fit_haldensify,
  Qn_pred_natural,
  Qn_pred_shifted,
  cv_folds = 10L,
  ci_level = 0.95,
  l1norm_mult = 10L
)

Arguments

W

A matrix, data.frame, or similar containing a set of baseline covariates.

A

A numeric vector corresponding to a exposure variable. The parameter of interest is defined as a location shift of this quantity.

Y

A numeric vector of the observed outcomes.

delta

A numeric value indicating the shift in the exposure to be used in defining the target parameter. This is defined with respect to the scale of the exposure (A).

gn_pred_natural

A matrix of conditional density estimates of the exposure mechanism g(A|W) along a grid of the regularization parameter, at the natural (observed, actual) values of the exposure.

gn_pred_shifted

A matrix of conditional density estimates of the exposure mechanism g(A+delta|W) along a grid of the regularization parameter, at the shifted (counterfactual) values of the exposure.

gn_fit_haldensify

An object of class haldensify of the fitted conditional density model for the natural exposure mechanism. This should be the fit object returned by haldensify[haldensify] as part of a call to ipw_shift.

Qn_pred_natural

A numeric of the outcome mechanism estimate at the natural (i.e., observed) values of the exposure. HAL regression is used for the estimate, with the regularization term chosen by cross-validation.

Qn_pred_shifted

A numeric of the outcome mechanism estimate at the shifted (i.e., counterfactual) values of the exposure. HAL regression is used for the estimate, with the regularization term chosen by cross-validation.

cv_folds

A numeric giving the number of folds to be used for cross-validation. Note that this form of sample splitting is used for the selection of tuning parameters by empirical risk minimization, not for the estimation of nuisance parameters (i.e., to relax regularity conditions).

ci_level

A numeric indicating the confidence level to be used in determining the cutoff used by the Lepski-type selector. This is only exposed for the sake of accommodating experimentation.

l1norm_mult

A numeric indicating the multipler to be used by the plateau-based selector in reducing the candidate set of L1 norms relative to the choice made by the cross-validation selector.


nhejazi/haldensify documentation built on Feb. 23, 2024, 8:25 a.m.