average_partial_effect

See the function `average_treatment_effect`

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

average_partial_effect function

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

Arguments

Average partial effect (removed) — average_partial_effect

<dl>

<dt>forest</dt>
<dd>The forest</dd>


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

</dl>

average_partial_effect: Average partial effect (removed)

Description

Usage

Value

Arguments