moi_missing: Mean Imputation Among Missing Outcomes

In targeted: Targeted Inference

Mean Imputation Among Missing Outcomes

Description

Estimates the mean of a given parametric imputation model among observations with a missing outcome and a given treatment. Specifically, it provides estimates of E[U(X,A,Z;\theta)|A=a, \Delta=0], for an imputation model U, where X denotes baseline covariates, A denotes the treatment, Z denotes post randomization covariates, and \Delta denotes a non-missing indicator. Influence function based standard errors are also provided.

Usage

moi_missing(
  data,
  id,
  delta,
  treatment.model,
  imputation.model,
  imputation.subset = NULL,
  imputation.augmentation = FALSE,
  missing.model = NULL,
  imputation.augmentation.model = NULL,
  extended.output = FALSE
)

Arguments

id	A vector with subject IDs
treatment.model	A learner object for the binary treatment, used to extract the treatment variable and its levels.
imputation.model	A learner object of class 'learner_glm' used to fit the imputation model. The learner must specify the outcome variable and model formula. If the learner was constructed with user-supplied weights, those weights are multiplied by the imputation.subset indicator (excluded rows receive zero weight).
missing.model	learner object specifying the model for the probability of the outcome being observed/non-missing
imputation.augmentation.model	learner object specifying the model for the imputation augmentation
extended.output	Logical. If TRUE, the returned list also includes the augmentation component IC3 of the influence function (only when imputation.augmentation = TRUE) and the imputation-model influence function IC_epsilon. Default is FALSE.

Value

A list with components:

estimate	A lava::estimate object with coefficients E[U|A=1,\Delta=0] and E[U|A=0,\Delta=0] and the associated influence functions.
imputation.model	The fitted imputation model.
imputation.subset	The imputation.subset expression.
levels	Treatment levels (character).
IC3	(only if extended.output = TRUE and imputation.augmentation = TRUE) Named list (one entry per treatment level) giving the per-level augmentation contribution to the influence function.
IC_epsilon	(only if extended.output = TRUE) Influence function for the imputation-model parameters.

targeted documentation built on July 15, 2026, 9:06 a.m.