runDynamic

Visualize Causal Tree and Treatment Effects via Shiny

Estimating heterogeneous treatment effects with tree-based machine
learning algorithms and visualizing estimated results in flexible and
presentation-ready ways. For more information, see Brand, Xu, Koch,
and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package
first started as a fork of the 'causalTree' package on 'GitHub' and we
greatly appreciate the authors for their extremely useful and free package.

Jiahui Xu

htetree

Causal Inference with Tree-Based Machine Learning Algorithms

Tanvi Shinkre

Jennie Brand

runDynamic function

<dl><dt>model</dt>
<dd>a tree model constructed by <code>hte_causalTree, hte_matchinleaves,
or hte_ipw</code>.</dd>
<dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>
<dt>outcomevariable</dt>
<dd>a character representing the column name
of the outcome variable.</dd>
<dt>treatment_indicator</dt>
<dd>a character representing the column name
for the treatment variable in the causal setup.</dd>
<dt>propensity_score</dt>
<dd>a character representing the column name of
the propensity score.</dd></dl>

Arguments

Visualize Causal Tree and Treatment Effects via Shiny — runDynamic

<dl>

<dt>model</dt>
<dd>a tree model constructed by <code>hte_causalTree, hte_matchinleaves,
or hte_ipw</code>.</dd>


<dt>data</dt>
<dd>a data frame containing the variables in the model.</dd>


<dt>outcomevariable</dt>
<dd>a character representing the column name
of the outcome variable.</dd>


<dt>treatment_indicator</dt>
<dd>a character representing the column name
for the treatment variable in the causal setup.</dd>


<dt>propensity_score</dt>
<dd>a character representing the column name of
the propensity score.</dd>

</dl>

runDynamic: Visualize Causal Tree and Treatment Effects via Shiny

Description

Usage

Value

Arguments