print.boosted_regression_forest

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.boosted_regression_forest function

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

Arguments

Print a boosted regression forest — print.boosted_regression_forest

<dl>

<dt>x</dt>
<dd>The boosted forest to print.</dd>


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

</dl>

Print a boosted regression forest

print.boosted_regression_forest: Print a boosted regression forest

Description

Usage

Arguments