est_Hn

An estimate of the exposure density (a generalized propensity
score) using the output provided by <code><a rd-options="" href="/link/est_g_exp?package=txshift&version=0.3.8" data-mini-rdoc="txshift::est_g_exp">est_g_exp</a></code>.

gn_exp

Estimate Auxiliary Covariate of Full Data Efficient Influence Function

Efficient estimation of the population-level causal effects of
stochastic interventions on a continuous-valued exposure. Both one-step and
targeted minimum loss estimators are implemented for the counterfactual mean
value of an outcome of interest under an additive modified treatment policy,
a stochastic intervention that may depend on the natural value of the
exposure. To accommodate settings with outcome-dependent two-phase
sampling, procedures incorporating inverse probability of censoring
weighting are provided to facilitate the construction of inefficient and
efficient one-step and targeted minimum loss estimators. The causal
parameter and its estimation were first described by D<c3><ad>az and van der Laan
(2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
estimation procedure and its application to data from two-phase sampling
designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
implementation is described in NS Hejazi and DC Benkeser (2020)
<doi:10.21105/joss.02447>. Estimation of nuisance parameters may be
enhanced through the Super Learner ensemble model in 'sl3', available for
download from GitHub using 'remotes::install_github("tlverse/sl3")'.

Nima Hejazi

txshift

Efficient Estimation of the Causal Effects of Stochastic
Interventions

David Benkeser

Iv<c3><a1>n D<c3><ad>az

Jeremy Coyle

Mark van der Laan

est_Hn function

An estimate of the exposure density (a generalized propensity
score) using the output provided by <code><a rd-options='' href='est_g_exp'>est_g_exp</a></code>.

est_Hn: Estimate Auxiliary Covariate of Full Data Efficient Influence Function

Description

Usage

Arguments

Value

Details