plot_causens

This function plots 1) the ATE as a function of the sensitivity
function value when it is constant and 2) its associate 95
interval obtained via bootstrapping, if desired. The latter process can be
take a few seconds to minutes.

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

plot_causens function

<dl><dt>trt_model</dt>
<dd>The treatment model object as a formula or fitted glm.</dd>
<dt>data</dt>
<dd>A data frame containing the variables of interest.</dd>
<dt>outcome</dt>
<dd>The name of the outcome variable.</dd>
<dt>c1_upper</dt>
<dd>The upper bound for the sensitivity function value.</dd>
<dt>c1_lower</dt>
<dd>The lower bound for the sensitivity function value.</dd>
<dt>r</dt>
<dd>The ratio between c1 and c0.</dd>
<dt>by</dt>
<dd>The increment for the sensitivity function value.</dd></dl>

Arguments

Plot ATE with respect to sensitivity function value
when it is constant, i.e. c(1, e) = c1 and c(0, e) = c0. — plot_causens

<dl>

<dt>trt_model</dt>
<dd>The treatment model object as a formula or fitted glm.</dd>


<dt>data</dt>
<dd>A data frame containing the variables of interest.</dd>


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


<dt>c1_upper</dt>
<dd>The upper bound for the sensitivity function value.</dd>


<dt>c1_lower</dt>
<dd>The lower bound for the sensitivity function value.</dd>


<dt>r</dt>
<dd>The ratio between c1 and c0.</dd>


<dt>by</dt>
<dd>The increment for the sensitivity function value.</dd>

</dl>

plot_causens: Plot ATE with respect to sensitivity function value when it is constant, i.e. c(1, e) = c1 and c(0, e) = c0.

Description

Usage

Value

Arguments