tmle_medshift: TML Estimator for the Population Intervention (In)direct...
In tlverse/tmle3mediate: Targeted Learning for Causal Mediation Analysis

O = (W, A, Z, Y) W = Covariates (possibly multivariate) A = Treatment (binary or categorical) Z = Mediators (binary or categorical; possibly multivariate) Y = Outcome (binary or bounded continuous)

tmle_medshift(
  shift_type = "ipsi",
  delta,
  e_learners,
  phi_learners,
  max_iter = 10000,
  step_size = 1e-06,
  ...
)

`shift_type`	A `character` defining the type of shift to be applied to the exposure – an incremental propensity score intervention.
`delta`	A `numeric`, specifying the magnitude of the shift.
`e_learners`	A `Stack` (or other learner class that inherits from `Lrnr_base`), containing a single or set of instantiated learners from sl3, to be used in fitting a cleverly parameterized propensity score that conditions on the mediators, i.e., e = P(A \mid Z, W).
`phi_learners`	A `Stack` (or other learner class that inherits from `Lrnr_base`), containing a single or set of instantiated learners from sl3, to be used in a regression of a pseudo-outcome on the baseline covariates, i.e., phi(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) \| W)].
`max_iter`	A `numeric` setting the maximum iterations allowed in the targeting step based on universal least favorable submodels.
`step_size`	A `numeric` giving the step size (`delta_epsilon` in `tmle3`) to be used in the targeting step based on universal least favorable submodels.
`...`	Additional arguments (currently unused).