data_validation: Represent validation causal data

In multibias: Multiple Bias Analysis in Causal Inference

Represent validation causal data

Description

data_validation is one of two different options to represent bias assumptions for bias adjustment. It combines the validation dataframe with specific identification of the appropriate columns for bias adjustment, including: true exposure, true outcome, confounders, misclassified exposure, misclassified outcome, and selection. The purpose of validation data is to use an external data source to transport the necessary causal relationships that are missing in the observed data.

Usage

data_validation(
  data,
  true_exposure,
  true_outcome,
  confounders = NULL,
  misclassified_exposure = NULL,
  misclassified_outcome = NULL,
  selection = NULL
)

Arguments

data	Dataframe of validation data
true_exposure	String name of the column in data corresponding to the true exposure.
true_outcome	String name of the column in data corresponding to the true outcome.
confounders	String name(s) of the column(s) in data corresponding to the confounding variable(s).
misclassified_exposure	String name of the column in data corresponding to the misclassified exposure.
misclassified_outcome	String name of the column in data corresponding to the misclassified outcome.
selection	String name of the column in data corresponding to the selection indicator.

Value

An object of class data_validation containing:

data	A dataframe with the selected columns
true_exposure	The name of the true exposure variable
true_outcome	The name of the true outcome variable
confounders	The name(s) of the confounder variable(s)
misclassified_exposure	The name of the misclassified exposure variable
misclassified_outcome	The name of the misclassified outcome variable
selection	The name of the selection indicator variable

Examples

df <- data_validation(
  data = df_sel_source,
  true_exposure = "X",
  true_outcome = "Y",
  confounders = c("C1", "C2", "C3"),
  selection = "S"
)

multibias documentation built on April 21, 2026, 1:06 a.m.