Nonparametric estimation of decomposition term for causal mediation analysis with stochastic interventions

medshift(W, A, Z, Y, delta, g_learners = sl3::Lrnr_glm_fast$new(family = stats::binomial()), e_learners = sl3::Lrnr_glm_fast$new(family =
stats::binomial()), m_learners = sl3::Lrnr_glm_fast$new(), phi_learners = sl3::Lrnr_glm_fast$new(), estimator = c("onestep",
"tmle", "substitution", "reweighted"), estimator_args = list(cv_folds =
10, max_iter = 10000, step_size = 1e-06))

## Arguments

W A matrix, data.frame, or similar corresponding to a set of baseline covariates. A numeric vector corresponding to a treatment variable. The parameter of interest is defined as a location shift of this quantity. A numeric vector, matrix, data.frame, or similar corresponding to a set of mediators (on the causal pathway between the intervention A and the outcome Y). A numeric vector corresponding to an outcome variable. A numeric value indicating the degree of shift in the intervention to be used in defining the causal quantity of interest. In the case of binary interventions, this takes the form of an incremental propensity score shift, acting as a multiplier of the probability with which a given observational unit receives the intervention (EH Kennedy, 2018, JASA; ). A Stack object, or other learner class (inheriting from Lrnr_base), containing a single or set of instantiated learners from the sl3 package, used in fitting a model for the propensity score, i.e., g = P(A | W). A Stack object, or other learner class (inheriting from Lrnr_base), containing a single or set of instantiated learners from the sl3 package, to be used in fitting a cleverly parameterized propensity score that includes the mediators, i.e., e = P(A | Z, W). A Stack object, or other learner class (inheriting from Lrnr_base), containing a single or set of instantiated learners from the sl3 package, to be used in fitting the outcome regression, i.e., m(A, Z, W). A Stack object, or other learner class (inheriting from Lrnr_base), containing a single or set of instantiated learners from the sl3 package, to be used in fitting a reduced regression useful for computing the efficient one-step estimator, i.e., phi(W) = E[m(A = 1, Z, W) - m(A = 0, Z, W) | W). The desired estimator of the natural direct effect to be computed. Currently, choices are limited to a substitution estimator, a re-weighted estimator, and an efficient one-step estimator. The interested user should consider consulting Díaz & Hejazi (2019+) for a comparative investigation of each of these estimators. A list of extra arguments to be passed (via ...) to the function call for the specified estimator. The default is so chosen as to allow the number of folds used in computing the AIPW estimator to be easily tweaked. Refer to the documentation for functions est_onestep, est_ipw, and est_substitution for details on what other arguments may be specified through this mechanism. For the option "tmle", there is heavy reliance on the architecture provided by the tmle3 package.