print.grf_tree

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_tree function

<dl><dt>x</dt>
<dd>The tree to print.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print a GRF tree object. — print.grf_tree

<dl>

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


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

</dl>

Print a GRF tree object.

print.grf_tree: Print a GRF tree object.

Description

Usage

Arguments