summary.bayesian_causens

Summarize the results of a causal sensitivity analysis via
Bayesian modelling of an unmeasured confounder.

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

summary.bayesian_causens function

<dl><dt>object</dt>
<dd>An object of class <code>bayesian_causens</code>.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to <code>summary</code>.</dd></dl>

Arguments

Summarize the results of a causal sensitivity analysis via
Bayesian modelling of an unmeasured confounder. — summary.bayesian_causens

<dl>

<dt>object</dt>
<dd>An object of class <code>bayesian_causens</code>.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to <code>summary</code>.</dd>

</dl>

summary.bayesian_causens: Summarize the results of a causal sensitivity analysis via Bayesian modelling of an unmeasured confounder.

Description

Usage

Value

Arguments