est_g_cens: Estimate the Censoring Mechanism
In txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Description Usage Arguments Details Value

Estimate the Censoring Mechanism

est_g_cens(
  C_cens,
  A,
  W,
  samp_weights = rep(1, length(C_cens)),
  fit_type = c("sl", "glm"),
  glm_formula = "C_cens ~ .",
  sl_learners = NULL
)

`C_cens`	A `numeric` vector of loss to follow-up indicators.
`A`	A `numeric` vector of observed exposure values.
`W`	A `numeric` matrix of observed baseline covariate values.
`samp_weights`	A `numeric` vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism `est_samp`.
`fit_type`	A `character` indicating whether to use GLMs or Super Learner to fit the censoring mechanism. If option "glm" is selected, the argument `glm_formula` must NOT be `NULL`, instead containing a model formula (as per `glm`) as a `character`. If the option "sl" is selected, the argument `sl_learners` must NOT be `NULL`; instead, an instantiated sl3 `Lrnr_sl` object, specifying learners and a metalearner for the Super Learner fit, must be provided. Consult the documentation of sl3 for details.
`glm_formula`	A `character` giving a `formula` for fitting a (generalized) linear model via `glm`.
`sl_learners`	Object containing a set of instantiated learners from the sl3, to be used in fitting an ensemble model.