get_scores.causal_survival_forest

For details see section 3.2 in the causal survival forest paper.

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.causal_survival_forest function

<dl><dt>forest</dt>
<dd>A trained causal survival forest.</dd>
<dt>subset</dt>
<dd>Specifies subset of the training examples over which we
estimate the ATE. WARNING: For valid statistical performance,
the subset should be defined only using features Xi, not using
the treatment Wi or the outcome Yi.</dd>
<dt>num.trees.for.weights</dt>
<dd>Number of trees used to estimate Var[W | X = x]. Note: this
argument is only used in the case of a continuous treatment
(see <code>get_scores.causal_forest</code> for details).</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Compute doubly robust scores for a causal survival forest. — get_scores.causal_survival_forest

<dl>

<dt>forest</dt>
<dd>A trained causal survival forest.</dd>


<dt>subset</dt>
<dd>Specifies subset of the training examples over which we
estimate the ATE. WARNING: For valid statistical performance,
the subset should be defined only using features Xi, not using
the treatment Wi or the outcome Yi.</dd>


<dt>num.trees.for.weights</dt>
<dd>Number of trees used to estimate Var[W | X = x]. Note: this
argument is only used in the case of a continuous treatment
(see <code>get_scores.causal_forest</code> for details).</dd>


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

</dl>

get_scores.causal_survival_forest: Compute doubly robust scores for a causal survival forest.

Description

Usage

Value

Arguments