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_NIE(e_learners, psi_Z_learners, max_iter = 10000, step_size = 1e-06, ...)
Arguments
| 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). |