makeplots

An intermediate function used for plotting

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

makeplots function

<dl><dt>negative</dt>
<dd>a logical value indicating whether we expect the
treatment effect to be negative. The default is set as FALSE.</dd>
<dt>opfit.</dt>
<dd>tree structure generated from causal tree algorithm.</dd>
<dt>trainset</dt>
<dd>a data frame only containing the variables used in
the model and missings values are listwise deleted.</dd>
<dt>covariates</dt>
<dd>a vector of column names of all
covariates (linear terms andpropensity score).</dd>
<dt>outcomevariable</dt>
<dd>a character representing the column name
of the outcome variable.</dd>
<dt>data.</dt>
<dd>a data frame containing the variables in the model.</dd>
<dt>hte_effect_setup</dt>
<dd>a empty list to store the adjusted treatment
effect.</dd>
<dt>varlabel</dt>
<dd>a named vector containing variable labels.</dd>
<dt>maintitle</dt>
<dd>a character string indicating the main title displayed
when plotting the tree and results. The default is set as
"Heterogeneous Treatment Effect Estimation".</dd>
<dt>legend.x, legend.y</dt>
<dd>x and y coordinate to position the legend.
The default is set as (0.08, 0.25).</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Visualize Causal Tree and the Estimated Results — makeplots

<dl>

<dt>negative</dt>
<dd>a logical value indicating whether we expect the
treatment effect to be negative. The default is set as FALSE.</dd>


<dt>opfit.</dt>
<dd>tree structure generated from causal tree algorithm.</dd>


<dt>trainset</dt>
<dd>a data frame only containing the variables used in
the model and missings values are listwise deleted.</dd>


<dt>covariates</dt>
<dd>a vector of column names of all
covariates (linear terms andpropensity score).</dd>


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


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


<dt>hte_effect_setup</dt>
<dd>a empty list to store the adjusted treatment
effect.</dd>


<dt>varlabel</dt>
<dd>a named vector containing variable labels.</dd>


<dt>maintitle</dt>
<dd>a character string indicating the main title displayed
when plotting the tree and results. The default is set as
"Heterogeneous Treatment Effect Estimation".</dd>


<dt>legend.x, legend.y</dt>
<dd>x and y coordinate to position the legend.
The default is set as (0.08, 0.25).</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

makeplots: Visualize Causal Tree and the Estimated Results

Description

Usage

Value

Arguments