est.causalTree

Run down the built tree and get the final leaf ids for 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

est.causalTree function

<dl><dt>fit</dt>
<dd>an <code>causalTree</code> object</dd>
<dt>x</dt>
<dd>covariates</dd></dl>

Arguments

Intermediate function for causalTree — est.causalTree

<dl>

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


<dt>x</dt>
<dd>covariates</dd>

</dl>

est.causalTree: Intermediate function for `causalTree`

Description

Usage

Value

Arguments