dcar_selector: Targeted IPW Estimator Selector via Solving the Efficient...
In nhejazi/haldensify: Highly Adaptive Lasso Conditional Density Estimation

dcar_selector

R Documentation

Targeted IPW Estimator Selector via Solving the Efficient Influence Function

Targeted IPW Estimator Selector via Solving the Efficient Influence Function

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

`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.