Fit pseudo-outcome regression conditioning on mediator-outcome confounder

Fit pseudo-outcome regression conditioning on mediator-outcome confounder

fit_nuisance_u(
  train_data,
  valid_data,
  learners,
  b_out,
  q_out,
  r_out,
  g_out,
  h_out,
  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.

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 mediators, i.e., fit_moc_mech, setting type = "q".

r_out

Output from the internal function for fitting the mechanism of the intermediate confounder without conditioning on mediators, i.e., fit_moc_mech, setting type = "r".

g_out

Output from the internal function for fitting the treatment mechanism without conditioning on mediators fit_treat_mech.

h_out

Output from the internal function for fitting the treatment mechanism conditioning on the mediators fit_treat_mech.

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.