simulate_data: Generate data with unmeasured confounder

In causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods

Generate data with unmeasured confounder

Description

Generate data with unmeasured confounder

Usage

simulate_data(
  ymodel = "linear",
  N = 500,
  u_type = "binary",
  y_type = "continuous",
  seed = 123,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  informative_u = FALSE
)

Arguments

ymodel	A string indicating the functional form of the outcome model.
N	The number of observations to be generated.
u_type	A string indicating the type of the unmeasured confounder: "binary" or "continuous".
y_type	A string indicating the type of the outcome: "binary" or "continuous".
seed	The seed for the random number generator.
alpha_uz	Unmeasured confounder coefficient in the propensity score model.
beta_uy	Unmeasured confounder coefficient in the outcome model.
treatment_effects	The treatment effect.
informative_u	A boolean indicating whether the unmeasured confounder is driven by covariates.

Value

A data frame with the simulated dataset.

causens documentation built on June 8, 2025, 10:03 a.m.