tmle_NIE: TML Estimator for the Natural Indirect Effect
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)

1	tmle_NIE(e_learners, psi_Z_learners, max_iter = 10000, step_size = 1e-06, ...)

`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).
`psi_Z_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., psi_Z(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) \mid A = 0, 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).