importance

Each primary split is credited with the value of splits$improve
Each surrogate split gets split$adj times the primary split's value

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

importance function

<dl><dt>fit</dt>
<dd>a fitted <code>causalTree</code> object.</dd></dl>

Arguments

Caclulate variable importance — importance

<dl>

<dt>fit</dt>
<dd>a fitted <code>causalTree</code> object.</dd>

</dl>

importance: Caclulate variable importance

Description

Usage

Value

Arguments