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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")'.

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VersionRepositoryFileSize
0.3.8 rolling linux/jammy R-4.5 txshift_0.3.8.tar.gz 154.1 KiB
0.3.8 rolling linux/noble R-4.5 txshift_0.3.8.tar.gz 154.3 KiB
0.3.8 rolling source/ R- txshift_0.3.8.tar.gz 70.3 KiB
0.3.8 latest linux/jammy R-4.5 txshift_0.3.8.tar.gz 154.1 KiB
0.3.8 latest linux/noble R-4.5 txshift_0.3.8.tar.gz 154.3 KiB
0.3.8 latest source/ R- txshift_0.3.8.tar.gz 70.3 KiB
0.3.8 2026-04-26 source/ R- txshift_0.3.8.tar.gz 70.3 KiB
0.3.8 2026-04-23 source/ R- txshift_0.3.8.tar.gz 70.3 KiB
0.3.8 2026-04-09 windows/windows R-4.5 txshift_0.3.8.zip 158.8 KiB
0.3.8 2025-04-20 source/ R- txshift_0.3.8.tar.gz 70.3 KiB

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