Transform a hunted function \(\hat{h}\) into a debiased function \(\hat{h} - \hat{m}_{\hat{h}}\), where \(\hat{m}_{\hat{h}}\) is the projection of \(\hat{h}\) onto the null model.
Usage
debias_standard(
h.hat,
X.debias,
fit.debias,
predict_fun,
weight_fun,
wls_method,
arg.wls_method
)Arguments
- h.hat
A list as returned by one of
hunt_optimal(),hunt_wls(),hunt_vanilla().- X.debias
Part of X for debiasing.
- fit.debias
Null model fitted on the debiasing sample of X and y.
- predict_fun, weight_fun, wls_method, arg.wls_method
They must be compatible with
fit.debias; seedScoreTest()for details.
Value
A list with elements:
m.h.fitThe null model fitted (over all columns of
X) to project and debias \(\hat{h}\).hThe debiased hunt function \(\hat{h} - \hat{m}_{\hat{h}}\) with signature
h(X).
Details
The projection \(\hat{m}_{\hat{h}}\) is obtained by fitting the null model
(via wls_method, weighted by weight_fun(fit.debias, X.debias))
with the hunted values h.hat$h(X.debias) as response. This projection
uses all columns of X, even when the hunt itself is driven by
only a subset of covariates (h.hat$X.cols).