expected_survival

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

expected_survival function

<dl><dt>S.hat</dt>
<dd>The estimated survival curve.</dd>
<dt>Y.grid</dt>
<dd>The time values corresponding to S.hat.</dd></dl>

Arguments

Compute E[T | X] — expected_survival

<dl>

<dt>S.hat</dt>
<dd>The estimated survival curve.</dd>


<dt>Y.grid</dt>
<dd>The time values corresponding to S.hat.</dd>

</dl>

Compute E[T | X]

expected_survival: Compute E[T | X]

Description

Usage

Value

Arguments