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 DÃaz 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 DÃaz [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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=txshift to link to this page.
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