Fit pseudo-outcome regression conditioning on treatment and baseline

Usage

fit_nuisance_v(
  train_data,
  valid_data,
  contrast,
  learners,
  b_out,
  q_out,
  m_names,
  w_names
)

Arguments

train_data: A data.table containing observed data, with columns in the order specified by the NPSEM (Y, M, R, Z, A, W), with column names set appropriately based on the input data. Such a structure is a convenience utility to passing data around to the various core estimation routines and is automatically generated by medoutcon.
valid_data: A holdout data set, with columns exactly matching those appearing in the preceding argument data, to be used for estimation via cross-fitting. Not optional for this nuisance parameter.
contrast: A numeric double indicating the two values of the intervention A to be compared. The default value of c(0, 1) assumes a binary intervention node A.
learners: Stack, or other learner class (inheriting from Lrnr_base), containing a set of learners from sl3, to be used in fitting a model for this nuisance parameter.
b_out: Output from the internal function for fitting the outcome regression fit_out_mech.
q_out: Output from the internal function for fitting the mechanism of the intermediate confounder while conditioning on the mediators, i.e., fit_moc_mech, setting type = "q".
m_names: A character vector of the names of the columns that correspond to mediators (M). The input for this argument is automatically generated by a call to the wrapper function medoutcon.
w_names: A character vector of the names of the columns that correspond to baseline covariates (W). The input for this argument is automatically generated by medoutcon.