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")'.

Version:	0.3.4
Depends:	R (≥ 3.2.0)
Imports:	stats, stringr, data.table, assertthat, mvtnorm, hal9001 (≥ 0.2.6), haldensify (≥ 0.0.6), lspline, ggplot2, tibble, latex2exp, Rdpack
Suggests:	testthat, knitr, rmarkdown, covr, future, future.apply, origami (≥ 1.0.3), ranger, Rsolnp, nnls, rlang
Enhances:	sl3 (≥ 1.3.7)
Published:	2020-09-25
Author:	Nima Hejazi [aut, cre, cph], David Benkeser [aut], Iván Díaz [ctb], Jeremy Coyle [ctb], Mark van der Laan [ctb, ths]
Maintainer:	Nima Hejazi <nh at nimahejazi.org>
BugReports:	https://github.com/nhejazi/txshift/issues
License:	MIT + file LICENSE
URL:	https://github.com/nhejazi/txshift
NeedsCompilation:	no
Citation:	txshift citation info
Materials:	README NEWS
CRAN checks:	txshift results

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