fit_e_mech: Fit propensity score regression while conditioning on...
In nhejazi/medshift: Causal mediation analysis for stochastic interventions

Fit propensity score regression while conditioning on mediators

1	fit_e_mech(data, valid_data = NULL, learners, z_names, w_names)

`data`	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`.
`valid_data`	A holdout data set, with columns exactly matching those appearing in the preceding argument `data`, to be used for estimation via cross-fitting. Optional, defaulting to `NULL`.
`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 propensity score that conditions on the mediators, i.e., e = P(A \| Z, W).
`z_names`	A `character` vector of the names of the columns that correspond to mediators (Z). The input for this argument is automatically generated by `medshift`.
`w_names`	A `character` vector of the names of the columns that correspond to baseline covariates (W). The input for this argument is automatically generated by a call to the wrapper function `medshift`.