model.frame.causalTree

get model frame of causalTree, same as rpart

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

model.frame.causalTree function

<dl><dt>formula</dt>
<dd>a <a href="/link/formula?package=htetree&version=0.1.20" data-mini-rdoc="htetree::formula">formula</a>, with a response but no interaction terms.
If this is a data frame, it is taken as the model frame (see
<a href="/link/model.frame?package=htetree&version=0.1.20" data-mini-rdoc="htetree::model.frame">model.frame</a>).</dd>
<dt>...</dt>
<dd>arguments to <code>rpart.control</code> may also be
specified in the call to <code>causalTree</code>. They are checked against the
list of valid arguments. An example of a commonly set parameter would
be <code>xval</code>, which sets the number of cross-validation samples.
The parameter <code>minsize</code> is implemented differently in
<code>causalTree</code> than in <code>rpart</code>; we require a minimum of <code>minsize</code>
treated observations and a minimum of <code>minsize</code> control
observations in each leaf.</dd></dl>

Arguments

Intermediate function for causalTree — model.frame.causalTree

<dl>

<dt>formula</dt>
<dd>a <a href='https://rdrr.io/r/stats/formula.html'>formula</a>, with a response but no interaction terms.
If this is a data frame, it is taken as the model frame (see
<a href='https://rdrr.io/r/stats/model.frame.html'>model.frame</a>).</dd>


<dt>...</dt>
<dd>arguments to <code>rpart.control</code> may also be
specified in the call to <code>causalTree</code>. They are checked against the
list of valid arguments. An example of a commonly set parameter would
be <code>xval</code>, which sets the number of cross-validation samples.
The parameter <code>minsize</code> is implemented differently in
<code>causalTree</code> than in <code>rpart</code>; we require a minimum of <code>minsize</code>
treated observations and a minimum of <code>minsize</code> control
observations in each leaf.</dd>

</dl>

model.frame.causalTree: Intermediate function for `causalTree`

Description

Usage

Value

Arguments