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).
|
gcv_mult |
TODO
|
bootstrap |
A logical indicating whether the estimator variance
should be approximated using the nonparametric bootstrap. The default is
FALSE , in which case the empirical variances of the IPW estimating
function and the EIF are used for for estimator selection and for variance
estimation, respectively. When set to TRUE , the bootstrap variance
is used for both of these purposes instead. Note that the bootstrap is very
computationally intensive and scales relatively poorly.
|
n_boot |
A numeric giving the number of bootstrap re-samples to
be used in computing the plateau estimator selection criterion. The default
uses 1000 bootstrap samples, though it may be appropriate to use fewer such
samples for experimentation purposes. This is ignored when bootstrap
is set to FALSE (its default).
|
... |
Additional arguments for model fitting to be passed directly to
haldensify .
|