Fit intermediate confounding mechanism with(out) conditioning on mediators

fit_moc_mech(
  train_data,
  valid_data = NULL,
  contrast,
  learners,
  m_names,
  w_names,
  type = c("q", "r")
)

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. Optional, defaulting to NULL.
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 the intermediate confounding mechanism, i.e., q = E[z|a',W] and r = E[z|a',m,w]).
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.
type: A character vector indicating whether to condition on the mediators (M) or not. Specifically, this is an option for specifying one of two types of nuisance regressions: "r" is defined as the component that conditions on the mediators (i.e., r = E[z|a',m,w]) while "q" is defined as the component that does not (i.e., q = E[z|a',w]).