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README.Rmd
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---
output:
rmarkdown::github_document
bibliography: "inst/REFERENCES.bib"
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# R/`tmle3shift`
[](https://github.com/tlverse/tmle3shift/actions)
[](https://codecov.io/gh/tlverse/tmle3shift)
[](http://www.repostatus.org/#active)
[](http://www.gnu.org/licenses/gpl-3.0)
[](https://doi.org/10.5281/zenodo.4603372)
> Targeted Learning of the Causal Effects of Stochastic Interventions
__Authors:__ [Nima Hejazi](https://nimahejazi.org), [Jeremy
Coyle](https://github.com/jeremyrcoyle), and [Mark van der
Laan](https://vanderlaan-lab.org)
---
## What's `tmle3shift`?
`tmle3shift` is an adapter/extension R package in the `tlverse` ecosystem that
exposes support for the estimation of a target parameter defined as the mean
counterfactual outcome under a posited shift of the natural value of a
continuous-valued intervention, using the formalism of stochastic treatment
regimes. As an adapter package, `tmle3shift` builds upon the core `tlverse`
grammar introduced by `tmle3`, a general framework that supports the
implementation of a range of TMLE parameters through a unified interface. For a
detailed description of the target parameter, TML estimator, and algorithm
implemented in `tmle3shift`, the interested reader is invited to consult
@diaz2012population and @diaz2018stochastic. For a general discussion of the
framework of targeted minimum loss-based estimation and the role this
methodology plays in statistical and causal inference, the canonical references
are @vdl2011targeted and @vdl2018targeted.
Building on the original work surrounding the TML estimator for the
aforementioned target parameter, `tmle3shift` additionally implements a set of
techniques for variable importance analysis, allowing for a sequence of mean
counterfactual outcomes, estimated under a sequence of posited shifts, to be
summarized via a working marginal structural model (MSM). The goal of this work
is to build upon the `tlverse` framework and the estimation methodology
implemented for a single mean counterfactual outcome in order to introduce an
end-to-end methodology for variable importance analyses.
---
## Installation
You can install the development version of `tmle3shift` from GitHub via
[`remotes`](https://CRAN.R-project.org/package=remotes) with
```{r gh-installation, eval = FALSE}
remotes::install_github("tlverse/tmle3shift")
```
---
## Issues
If you encounter any bugs or have any specific feature requests, please [file an
issue](https://github.com/tlverse/tmle3shift/issues).
---
## Contributions
Contributions are very welcome. Interested contributors should consult our
[contribution
guidelines](https://github.com/tlverse/tmle3shift/blob/master/CONTRIBUTING.md)
prior to submitting a pull request.
---
## Citation
After using the `tmle3shift` R package, please cite the following:
@software{hejazi2021tmle3shift-rpkg,
author = {Hejazi, Nima S and Coyle, Jeremy R and {van der Laan}, Mark
J},
title = {{tmle3shift}: {Targeted Learning} of the Causal Effects of
Stochastic Interventions},
year = {2021},
howpublished = {\url{https://github.com/tlverse/tmle3shift}},
note = {{R} package version 0.2.0},
url = {https://doi.org/10.5281/zenodo.4603372},
doi = {10.5281/zenodo.4603372}
}
---
## Related
* [R/`txshift`](https://github.com/nhejazi/txshift) - An R package providing an
independent implementation of the TML estimation procedure and
statistical methodology as is made available here, without reliance on the
`tlverse` grammar provided by `tmle3`.
---
## Funding
The development of this software was supported in part through a grant from the
National Institutes of Health: [T32
LM012417-02](https://projectreporter.nih.gov/project_info_description.cfm?aid=9248418&icde=37849831&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&pball=).
---
## License
The contents of this repository are distributed under the GPL-3 license. See
file `LICENSE` for details.
---
## References