tmle_medshift: TML Estimator for the Counterfactual Mean of a Joint...
In nhejazi/medshift: Causal mediation analysis for stochastic interventions

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 2	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, by default.
`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 \| 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).