tune_causal_forest

To tune a causal forest, see the function `causal_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

tune_causal_forest function

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

Arguments

Causal forest tuning (removed) — tune_causal_forest

<dl>

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


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


<dt>W</dt>
<dd>W</dd>


<dt>Y.hat</dt>
<dd>Y.hat</dd>


<dt>W.hat</dt>
<dd>W.hat</dd>


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

</dl>

tune_causal_forest: Causal forest tuning (removed)

Description

Usage

Value

Arguments