Efficient estimation of the populationlevel causal effects of stochastic interventions on a continuousvalued exposure. Both onestep 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 twophase sampling is employed, procedures for making use of inverse probability of censoring weights are provided to facilitate construction of inefficient and efficient onestep and targeted minimum loss estimators. The causal parameter and estimation methodology were first described by Díaz and van der Laan (2013) <doi:10.1111/j.15410420.2011.01685.x>). 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")'.
Package details 


Author  Nima Hejazi [aut, cre, cph] (<https://orcid.org/0000000271272789>), David Benkeser [aut] (<https://orcid.org/0000000210198343>), Iván Díaz [ctb] (<https://orcid.org/0000000190562047>), Jeremy Coyle [ctb] (<https://orcid.org/0000000298746649>), Mark van der Laan [ctb, ths] (<https://orcid.org/0000000314325511>) 
Maintainer  Nima Hejazi <nh@nimahejazi.org> 
License  MIT + file LICENSE 
Version  0.3.4 
URL  https://github.com/nhejazi/txshift 
Package repository  View on CRAN 
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