target_conditional_direct_effect: The naming convention is a bit different here, because we're...
In benkeser/intermed:

The naming convention is a bit different here, because we're actually able to go after the conditional direct effect, well, directly. That is, we can define a loss function whose minimizer defines the conditional mean of the difference between Qbar_a and Qbar_a_star with respect to the joint distribution of M1 and M2 given C and A = a_star. We can then define a submodel through this conditional mean difference (which is exactly the conditional direct effect) and target this quantity directly.

target_conditional_direct_effect(
  Qbarbar,
  all_mediator_values,
  gn,
  Qbar,
  Y,
  A,
  a,
  a_star,
  M1,
  M2,
  target_conditional = TRUE,
  epsilon_threshold = 5,
  bound_pred = FALSE,
  universal = TRUE,
  deps = 1e-05,
  max_iter = 10000,
  ...
)

`Qbarbar`	Iterated mean estimates
`all_mediator_values`	All combinations of M1 and M2
`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`.
`Qbar`	Outcome regression estimates
`Y`	A vector of continuous or binary outcomes.
`A`	A vector of binary treatment assignment (assumed to be equal to 0 or 1).
`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`.
`M1`	A `vector` of mediators.
`M2`	A `vector` of mediators.
`epsilon_threshold`	To avoid extreme values of fluctuation parameters (indicating likely numerical instability), we truncate the value this parameter can take.
`bound_pred`	Should predictions be bounded?
`max_iter`	The maximum number of iterations for the TMLE