plateau_selector: Agnostic IPW Estimator Selector via Lepski's and...
In nhejazi/haldensify: Highly Adaptive Lasso Conditional Density Estimation

plateau_selector

R 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.