print.grf

Forest-based statistical estimation and inference.
GRF provides non-parametric methods for heterogeneous treatment effects estimation
(optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
as well as least-squares regression, quantile regression, and survival regression,
all with support for missing covariates.

Erik Sverdrup

Generalized Random Forests

Julie Tibshirani

Susan Athey

Rina Friedberg

Vitor Hadad

David Hirshberg

Luke Miner

Stefan Wager

Marvin Wright

print.grf function

<dl><dt>x</dt>
<dd>The tree to print.</dd>
<dt>decay.exponent</dt>
<dd>A tuning parameter that controls the importance of split depth.</dd>
<dt>max.depth</dt>
<dd>The maximum depth of splits to consider.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print a GRF forest object. — print.grf

<dl>

<dt>x</dt>
<dd>The tree to print.</dd>


<dt>decay.exponent</dt>
<dd>A tuning parameter that controls the importance of split depth.</dd>


<dt>max.depth</dt>
<dd>The maximum depth of splits to consider.</dd>


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

Print a GRF forest object.

print.grf: Print a GRF forest object.

Description

Usage

Arguments