target_Qbar: Target the outcome regression
In benkeser/intermed:

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,
  ...
)

`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.