R/tmle3shift

Targeted Learning of the Causal Effects of Stochastic Interventions

Authors: Nima Hejazi, Jeremy
Coyle, and Mark van der
Laan

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 Dı́az and van der Laan (2012)
and Dı́az and van der Laan (2018). 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 van der Laan and Rose (2011) and van der Laan and Rose
(2018).

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 with

remotes::install_github("tlverse/tmle3shift")

Issues

If you encounter any bugs or have any specific feature requests, please
file an issue.

Contributions

Contributions are very welcome. Interested contributors should consult
our contribution
guidelines
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 - 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.

License

The contents of this repository are distributed under the GPL-3 license.
See file LICENSE for details.

References