causens_sf

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

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

causens_sf function

<dl><dt>trt_model</dt>
<dd>A model formula specifying the treatment model.</dd>
<dt>outcome</dt>
<dd>The name of the outcome variable.</dd>
<dt>data</dt>
<dd>A data frame containing the exposure, outcome, and confounder
variables.</dd>
<dt>bootstrap</dt>
<dd>A logical indicating whether to perform bootstrap estimation
of the 95% confidence interval.</dd>
<dt>B</dt>
<dd>If the bootstrap argument is TRUE, the number of bootstrap samples
to generate.</dd>
<dt>seed</dt>
<dd>An integer to set the random seed for reproducibility.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the sensitivity function.</dd></dl>

Arguments

Bayesian Estimation of ATE Subject to Unmeasured Confounding — causens_sf

<dl>

<dt>trt_model</dt>
<dd>A model formula specifying the treatment model.</dd>


<dt>outcome</dt>
<dd>The name of the outcome variable.</dd>


<dt>data</dt>
<dd>A data frame containing the exposure, outcome, and confounder
variables.</dd>


<dt>bootstrap</dt>
<dd>A logical indicating whether to perform bootstrap estimation
of the 95% confidence interval.</dd>


<dt>B</dt>
<dd>If the bootstrap argument is TRUE, the number of bootstrap samples
to generate.</dd>


<dt>seed</dt>
<dd>An integer to set the random seed for reproducibility.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to the sensitivity function.</dd>

</dl>

causens_sf: Bayesian Estimation of ATE Subject to Unmeasured Confounding

Description

Usage

Value

Arguments