est_Q: Estimate the Outcome Regression

Description Usage Arguments Details Value

View source: R/fit_mechanisms.R

Description

Estimate the Outcome Regression

Usage

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

Arguments

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.

Details

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).

Value

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.