get_sample_weights

Retrieve forest weights (renamed to get_forest_weights)

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

get_sample_weights function

<dl><dt>forest</dt>
<dd>The trained forest.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Retrieve forest weights (renamed to get_forest_weights) — get_sample_weights

<dl>

<dt>forest</dt>
<dd>The trained forest.</dd>


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

</dl>

get_sample_weights: Retrieve forest weights (renamed to get_forest_weights)

Description

Usage

Arguments