txshift: Efficient Estimation of the Causal Effects of StochasticInterventions
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 + fileLICENSE |
| URL: | https://github.com/nhejazi/txshift |
| NeedsCompilation: | no |
| Citation: | txshift citation info |
| Materials: | README,NEWS |
| CRAN checks: | txshift results |
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