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 a causal parameter defined as the counterfactual mean of an outcome of interest under a stochastic intervention that may depend on the natural value of the exposure (i.e., a modified treatment policy). To accommodate settings in which two-phase sampling is employed, procedures for making use of inverse probability of censoring weights are provided to facilitate 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 arising in two-phase sampling designs was detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. 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: 0.3.5
Depends: R (≥ 3.2.0)
Imports: stats, stringr, data.table, assertthat, mvtnorm, hal9001 (≥ 0.2.6), haldensify (≥ 0.0.6), lspline, ggplot2, tibble, scales, latex2exp, Rdpack, cli
Suggests: testthat, knitr, rmarkdown, covr, future, future.apply, origami (≥ 1.0.3), ranger, Rsolnp, nnls, rlang
Enhances: sl3 (≥ 1.3.7)
Published: 2021-02-07
Author: Nima Hejazi ORCID iD [aut, cre, cph], David Benkeser ORCID iD [aut], Iván Díaz ORCID iD [ctb], Jeremy Coyle ORCID iD [ctb], Mark van der Laan ORCID iD [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


Reference manual: txshift.pdf
Vignettes: Targeted Learning with Stochastic Treatment Regimes
IPCW-TMLEs with Stochastic Treatment Regimes
Package source: txshift_0.3.5.tar.gz
Windows binaries: r-devel: txshift_0.3.5.zip, r-release: txshift_0.3.5.zip, r-oldrel: txshift_0.3.5.zip
macOS binaries: r-release: txshift_0.3.5.tgz, r-oldrel: txshift_0.3.5.tgz
Old sources: txshift archive


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