leaf_stats.causal_forest

Calculate summary stats given a set of samples for causal forests.

internal

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

leaf_stats.causal_forest function

<dl><dt>forest</dt>
<dd>The GRF forest</dd>
<dt>samples</dt>
<dd>The samples to include in the calculations.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Calculate summary stats given a set of samples for causal forests. — leaf_stats.causal_forest

<dl>

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


<dt>samples</dt>
<dd>The samples to include in the calculations.</dd>


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

</dl>

Calculate summary stats given a set of samples for causal forests.

leaf_stats.causal_forest: Calculate summary stats given a set of samples for causal forests.

Description

Usage

Value

Arguments