create_jags_model

Creates a JAGS model available as a string, or .txt file, where
priors are initialized to be uninformative by default.

While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).

Larry Dong

causens

Perform Causal Sensitivity Analyses Using Various Statistical
Methods

Yushu Zou

Kuan Liu

create_jags_model function

<dl><dt>binary_outcome</dt>
<dd>Boolean indicating whether the outcome is binary.</dd>
<dt>beta_uy</dt>
<dd>Prior distribution for the effect of the missing confounder U
on the outcome Y.</dd>
<dt>alpha_uz</dt>
<dd>Prior distribution for the effect of the missing confounder U
on the treatment assignment mechanism Z.
No inputs are given to this function (for now) since data-related information
is provided in jags.model() during model initialization.</dd></dl>

Arguments

Create an JAGS model for Bayesian sensitivity analysis — create_jags_model

<dl>

<dt>binary_outcome</dt>
<dd>Boolean indicating whether the outcome is binary.</dd>


<dt>beta_uy</dt>
<dd>Prior distribution for the effect of the missing confounder U
on the outcome Y.</dd>


<dt>alpha_uz</dt>
<dd>Prior distribution for the effect of the missing confounder U
on the treatment assignment mechanism Z.
No inputs are given to this function (for now) since data-related information
is provided in jags.model() during model initialization.</dd>

</dl>

create_jags_model: Create an JAGS model for Bayesian sensitivity analysis

Description

Usage

Arguments