bayesian_causens: Bayesian parametric sensitivity analysis for causal inference

In causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods

Bayesian parametric sensitivity analysis for causal inference

Description

This function runs a Bayesian sensitivity analysis for causal inference using JAGS or Stan as a backend. For now, only JAGS is supported.

Usage

bayesian_causens(
  trt_model,
  outcome_model,
  U_model,
  data,
  beta_uy = ~dunif(-2, 2),
  alpha_uz = ~dunif(-2, 2),
  backend = "jags",
  output_trace = FALSE,
  ...
)

Arguments

trt_model	The treatment model object as a formula.
outcome_model	The outcome model object as a formula.
U_model	The unmeasured confounder model object as a formula.
data	A data frame containing the exposure, outcome, and confounder variables.
beta_uy	Prior distribution for the effect of the missing confounder U on the outcome Y.
alpha_uz	Prior distribution for the effect of the missing confounder U on the treatment assignment mechanism Z.
backend	The backend to use for the sensitivity analysis. Currently only "jags" is supported.
output_trace	Whether to output the full trace of the MCMC sampler.
...	Additional arguments to be passed to the backend.

Value

A list of posterior samples for the causal effect of the exposure variable on the outcome, as well as the confounder-adjusted causal effect.

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