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 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 two-phase sampling is employed, procedures for making use of inverse probability of censoring weights are provided to facilitate construction of inefficient and efficient one-step 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.1541-0420.2011.01685.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

AuthorNima Hejazi [aut, cre, cph] (<https://orcid.org/0000-0002-7127-2789>), David Benkeser [aut] (<https://orcid.org/0000-0002-1019-8343>), Iván Díaz [ctb] (<https://orcid.org/0000-0001-9056-2047>), Jeremy Coyle [ctb] (<https://orcid.org/0000-0002-9874-6649>), Mark van der Laan [ctb, ths] (<https://orcid.org/0000-0003-1432-5511>)
MaintainerNima Hejazi <nh@nimahejazi.org>
LicenseMIT + file LICENSE
URL https://github.com/nhejazi/txshift
Package repositoryView on CRAN
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txshift documentation built on Oct. 23, 2020, 8:27 p.m.