txshift: Efficient Estimation of the Causal Effects of Stochastic
Interventions
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 Díaz 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")'.
| Version: |
0.3.8 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
stats, stringr, data.table, assertthat, mvtnorm, hal9001 (≥
0.4.1), haldensify (≥ 0.2.1), lspline, ggplot2, scales, latex2exp, Rdpack |
| Suggests: |
testthat, knitr, rmarkdown, covr, future, future.apply, origami (≥ 1.0.3), ranger, Rsolnp, nnls |
| Enhances: |
sl3 (≥ 1.4.3) |
| Published: |
2022-02-09 |
| DOI: |
10.32614/CRAN.package.txshift |
| Author: |
Nima Hejazi  [aut,
cre, cph],
David Benkeser
[aut],
Iván Díaz [ctb],
Jeremy Coyle
[ctb],
Mark van der Laan
[ctb, ths] |
| Maintainer: |
Nima Hejazi <nh at nimahejazi.org> |
| BugReports: |
https://github.com/nhejazi/txshift/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/nhejazi/txshift |
| NeedsCompilation: |
no |
| Citation: |
txshift citation info |
| Materials: |
README NEWS |
| CRAN checks: |
txshift results |
Documentation:
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