haldensify (version 0.2.8)

dcar_selector: Targeted IPW Estimator Selector via Solving the Efficient Influence Function

Description

Targeted IPW Estimator Selector via Solving the Efficient Influence Function

Usage

dcar_selector(
  W,
  A,
  Y,
  delta = 0,
  gn_pred_natural,
  gn_pred_shifted,
  Qn_pred_natural,
  Qn_pred_shifted
)

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 (i.e., observed) 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 (i.e., counterfactual) values of the exposure.

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.