Target the outcome regression

target_Qbar(
  Y,
  A,
  M1,
  M2,
  a,
  a_star,
  all_mediator_values,
  Qbar_n,
  gn,
  Q_M_n,
  which_effects = c("direct", "indirectM1", "indirectM2"),
  bound_pred = FALSE,
  epsilon_threshold = 5,
  ...
)

Arguments

Y	A vector of continuous or binary outcomes.
A	A vector of binary treatment assignment (assumed to be equal to 0 or 1).
M1	A `vector` of mediators.
M2	A `vector` of mediators.
a	The label for the treatment. The effects estimates returned pertain to estimation of interventional effects of `a` versus `a_star`.
a_star	The label for the treatment. The effects estimates returned pertain to estimation of interventional effects of `a` versus `a_star`.
all_mediator_values	Grid of all combinations of M1 and M2
Qbar_n	Power users may wish to pass in their own properly formatted list of the outcome regression so that nuisance parameters can be fitted outside of `intermed`.
gn	Power users may wish to pass in their own properly formatted list of the propensity score so that nuisance parameters can be fitted outside of `intermed`.
Q_M_n	Power users may wish to pass in their own properly formatted list of the mediator distributions so that nuisance parameters can be fitted outside of `intermed`.
which_effects	Which effects to include in the targeted of the outcome regression
bound_pred	Should predictions be bounded?
epsilon_threshold	To avoid extreme values of fluctuation parameters (indicating likely numerical instability), we truncate the value this parameter can take.

Value

A list containing all outcome regression evaluations needed for downstream calculations.