causens_monte_carlo

This function performs a Monte Carlo sensitivity analysis for
causal effects.

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_monte_carlo function

<dl><dt>outcome</dt>
<dd>The name of the outcome variable in the data frame.</dd>
<dt>exposure</dt>
<dd>The name of the exposure variable in the data frame.</dd>
<dt>confounders</dt>
<dd>The name of the confounders in the data frame.</dd>
<dt>data</dt>
<dd>A data frame containing the exposure, outcome, and confounder
variables.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the function.</dd></dl>

Arguments

Monte Carlo sensitivity analysis for causal effects — causens_monte_carlo

<dl>

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


<dt>exposure</dt>
<dd>The name of the exposure variable in the data frame.</dd>


<dt>confounders</dt>
<dd>The name of the confounders in the data frame.</dd>


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


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

</dl>

causens_monte_carlo: Monte Carlo sensitivity analysis for causal effects

Description

Usage

Value

Arguments