onestep_txshift: One-Step Estimate of Counterfactual Mean of Stochastic Shift...
In txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions

Description Usage Arguments Details Value

One-Step Estimate of Counterfactual Mean of Stochastic Shift Intervention

onestep_txshift(
  data_internal,
  C_samp = rep(1, nrow(data_internal)),
  V = NULL,
  delta,
  samp_estim,
  gn_cens_weights,
  Qn_estim,
  Hn_estim,
  eif_reg_type = c("hal", "glm"),
  samp_fit_args,
  ipcw_efficiency = TRUE
)

`data_internal`	A `data.table` constructed internally by a call to `txshift`. This contains the data elements needed for computing the one-step estimator.
`C_samp`	A `numeric` indicator for whether a given observation was included in the second-stage sample, used to compute an IPC-weighted one-step estimator in cases where two-stage sampling is performed. Default assumes no censoring due to sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C` must be uniquely 1. To specify this, pass in a NAMED `list` identifying variables amongst W, A, Y that are thought to have played a role in defining the sampling/censoring mechanism (C).
`delta`	A `numeric` value indicating the shift in the treatment to be used in defining the target parameter. This is defined with respect to the scale of the treatment (A).
`samp_estim`	An object providing the value of the censoring mechanism evaluated across the full data. This object is passed in after being constructed by a call to the internal function `est_samp`.
`gn_cens_weights`	TODO: document
`Qn_estim`	An object providing the value of the outcome evaluated after imposing a shift in the treatment. This object is passed in after being constructed by a call to the internal function `est_Q`.
`Hn_estim`	An object providing values of the auxiliary ("clever") covariate, constructed from the treatment mechanism and required for targeted minimum loss estimation. This object object should be passed in after being constructed by a call to the internal function `est_Hn`.
`eif_reg_type`	Whether a flexible nonparametric function ought to be used in the dimension-reduced nuisance regression of the targeting step for the censored data case. By default, the method used is a nonparametric regression based on the Highly Adaptive Lasso (from hal9001). Set this to `"glm"` to instead use a simple linear regression model. In this step, the efficient influence function (EIF) is regressed against covariates contributing to the censoring mechanism (i.e., EIF ~ V \| C = 1).
`samp_fit_args`	A `list` of arguments, all but one of which are passed to `est_samp`. For details, consult the documentation for `est_samp`. The first element (i.e., `fit_type`) is used to determine how this regression is fit: "glm" for generalized linear model, "sl" for a Super Learner, and "external" for a user-specified input of the form produced by `est_samp`.
`ipcw_efficiency`	Whether to invoke an augmentation of the IPCW-TMLE procedure that performs an iterative process to ensure efficiency of the resulting estimate. The default is `TRUE`; set to `FALSE` to use an IPC-weighted loss rather than the IPC-augmented influence function.