Skip to contents

Bayesian Estimation of ATE Subject to Unmeasured Confounding

Source: R/causens_sf.R
causens_sf.Rd

This function provides an estimate of the Average Treatment Effect (ATE) using Bayesian modelling.

Usage

causens_sf(
  trt_model,
  outcome,
  data,
  bootstrap = FALSE,
  B = 1000,
  seed = 123,
  ...
)

Arguments

trt_model

A model formula specifying the treatment model.

outcome

The name of the outcome variable.

data

A data frame containing the exposure, outcome, and confounder variables.

bootstrap

A logical indicating whether to perform bootstrap estimation of the 95% confidence interval.

B

If the bootstrap argument is TRUE, the number of bootstrap samples to generate.

seed

An integer to set the random seed for reproducibility.

...

Additional arguments to be passed to the sensitivity function.

Value

A point estimate of the corrected ATE.