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txshift  

Efficient Estimation of the Causal Effects of Stochastic Interventions
View on CRAN: Click here


Download and install txshift package within the R console
Install from CRAN:
install.packages("txshift")

Install from Github:
library("remotes")
install_github("cran/txshift")

Install by package version:
library("remotes")
install_version("txshift", "0.3.8")



Attach the package and use:
library("txshift")
Maintained by
Nima Hejazi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-25
Latest Update: 2022-02-09
Description:
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")'.
How to cite:
Nima Hejazi (2020). txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions. R package version 0.3.8, https://cran.r-project.org/web/packages/txshift. Accessed 28 Oct. 2026.
Previous versions and publish date:
(2026-10-20 22:00), 0.3.4 (2020-09-25 15:50), 0.3.5 (2021-02-07 21:10), 0.3.6 (2021-10-18 20:10), 0.3.7 (2022-01-28 11:50), 0.3.8 (2022-02-09 23:30)
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