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) , 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) . The software package implementation is described in NS Hejazi and DC Benkeser (2020) . 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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install.packages("txshift")

0.3.6 by Nima Hejazi, 5 days ago


https://github.com/nhejazi/txshift


Report a bug at https://github.com/nhejazi/txshift/issues


Browse source code at https://github.com/cran/txshift


Authors: Nima Hejazi [aut, cre, cph] , David Benkeser [aut] , Iván Díaz [ctb] , Jeremy Coyle [ctb] , Mark van der Laan [ctb, ths]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports stats, stringr, data.table, assertthat, mvtnorm, hal9001, haldensify, lspline, ggplot2, scales, latex2exp, Rdpack

Suggests testthat, knitr, rmarkdown, covr, future, future.apply, origami, ranger, Rsolnp, nnls

Enhances sl3


See at CRAN