TML Estimator for the Counterfactual Mean of a Joint Stochastic Intervention Defining the Population Intervention (In)direct Effects

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, ...)

Arguments

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).