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

Description Usage Arguments Details Value

Estimate the Outcome Regression

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

`Y`	A `numeric` vector of observed outcomes.
`A`	A `numeric` vector of observed treatment values.
`W`	A `numeric` matrix of observed baseline covariate values.
`delta`	A `numeric` indicating the magnitude of the shift to be computed for the treatment `A`. This is passed to the internal `shift_additive` and is currently limited to additive shifts.
`ipc_weights`	A `numeric` vector of observation-level weights, as produced by the internal procedure to estimate the censoring mechanism.
`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 `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` corresponding to a `formula` to be used in 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 propensity score for the observed data (at A_i) and the shift (at A_i - 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 Oct. 23, 2020, 8:27 p.m.