Efficient One-Step Estimator
est_onestep(data, delta, g_learners, e_learners, m_learners, phi_learners,
w_names, z_names, cv_folds = 10)A data.table containing the observed data, with columns
in the order specified by the NPSEM (Y, Z, A, W), with column names set
appropriately based on the original input data. Such a structure is merely
a convenience utility to passing data around to the various core estimation
routines and is automatically generated by medshift.
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; doi:10.1080/01621459.2017.1422737).
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 the propensity
score, i.e., g = P(A | W).
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 propensity
score that conditions on the mediators, i.e., e = P(A | Z, W).
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 the outcome
regression, i.e., m(A, Z, W).
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).
A character vector of the names of the columns that
correspond to baseline covariates (W). The input for this argument is
automatically generated by medshift.
A character vector of the names of the columns that
correspond to mediators (Z). The input for this argument is automatically
generated by medshift.
A numeric specifying the number of folds to be
created for cross-validation. Use of cross-validation / cross-fitting
allows for entropy conditions on the AIPW estimator to be relaxed. Note:
for compatibility with make_folds, this value must
be greater than or equal to 2; the default is to create 10 folds.