get_scores

Compute doubly robust scores for a GRF forest object

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_scores function

<dl><dt>forest</dt>
<dd>A grf forest object</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Compute doubly robust scores for a GRF forest object — get_scores

<dl>

<dt>forest</dt>
<dd>A grf forest object</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

get_scores: Compute doubly robust scores for a GRF forest object

Description

Usage

Value

Arguments