est_Q: Estimate the Outcome Mechanism
In txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Description Usage Arguments Details Value

Estimate the Outcome Mechanism

est_Q(
  Y,
  C_cens = rep(1, length(Y)),
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(Y)),
  fit_type = c("sl", "glm"),
  glm_formula = "Y ~ .",
  sl_learners = NULL
)

`Y`	A `numeric` vector of observed outcomes.
`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.
`delta`	A `numeric` indicating the magnitude of the shift to be computed for the exposure `A`. This is passed to the internal `shift_additive` and is currently limited to additive shifts.
`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 outcome regression. If the 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.

Compute the outcome regression for the observed data, including with the shift imposed by the intervention. This returns the outcome regression for the observed data (at A) and under the counterfactual shift shift (at A + delta).

A data.table with two columns, containing estimates of the outcome mechanism at the natural value of the exposure Q(A, W) and an upshift of the exposure Q(A + delta, W).

txshift documentation built on Feb. 11, 2022, 1:08 a.m.