honest.est.causalTree

honest re-estimation and change the frame of object using estimation sample

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

honest.est.causalTree function

<dl><dt>fit</dt>
<dd>an <code>causalTree</code> object</dd>
<dt>x</dt>
<dd>input training data</dd>
<dt>wt</dt>
<dd>optional weights</dd>
<dt>treatment</dt>
<dd>treatment variable</dd>
<dt>y</dt>
<dd>outcome variable</dd></dl>

Arguments

honest re-estimation and change the frame of object using estimation sample — honest.est.causalTree

<dl>

<dt>fit</dt>
<dd>an <code>causalTree</code> object</dd>


<dt>x</dt>
<dd>input training data</dd>


<dt>wt</dt>
<dd>optional weights</dd>


<dt>treatment</dt>
<dd>treatment variable</dd>


<dt>y</dt>
<dd>outcome variable</dd>

</dl>

honest.est.causalTree: honest re-estimation and change the frame of object using estimation sample

Description

Usage

Value

Arguments