custom_forest

To build a custom forest, see an existing simpler forest, like regression_forest,
for a development template.

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

custom_forest function

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

Arguments

Custom forest (removed) — custom_forest

<dl>

<dt>X</dt>
<dd>X</dd>


<dt>Y</dt>
<dd>Y</dd>


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

</dl>

custom_forest: Custom forest (removed)

Description

Usage

Arguments