book.bib

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}

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@article{rivera2020guidelines,
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@article{liu2020reporting,
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}

@article{vinny2021critical,
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  volume={24},
  number={4},
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  year={2021},
  publisher={Wolters Kluwer--Medknow Publications}
}

@book{steyerberg2019clinical,
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  year={2019},
  publisher={Springer}
}

@article{nguyen2017double,
  title={Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance},
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  year={2017},
  publisher={Springer}
}

@article{alam2019should,
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  number={9},
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  year={2019},
  publisher={Wiley Online Library}
}

@article{keele2018pre,
  title={Pre-analysis Plan for a Comparison of Matching and Black Box-based Covariate Adjustment},
  author={Keele, Luke and Small, Dylan S},
  journal={Observational Studies},
  volume={4},
  number={1},
  pages={97--110},
  year={2018},
  publisher={University of Pennsylvania Press}
}


@article{austin2011tutorial,
  title={A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality},
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@article{gruber2010targeted,
  title={A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome},
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@article{luque2018targeted,
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@book{van2012targeted,
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}

@article{gruber2009targeted,
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@article{pirracchio2015improving,
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@article{schuler2017targeted,
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@article{naimi2017introduction,
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  year={2017},
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@article{zhong2021aipw,
  title={AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects},
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@article{snowden2011implementation,
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@article{austin2015moving,
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@article{rose2020intersections,
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@article{naimi2017challenges,
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  author={Naimi, Ashley I and Mishler, Alan E and Kennedy, Edward H},
  journal={arXiv preprint arXiv:1711.07137},
  year={2017}
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@article{balzer2021demystifying,
  title={Demystifying Statistical Inference When Using Machine Learning in Causal Research},
  author={Balzer, Laura B and Westling, Ted},
  journal={American Journal of Epidemiology},
  year={2021}
}

@manual{tmlePkgDocs,
  title={Package 'tmle'},
  author={Gruber, S. and Van Der Laan, M. and Kennedy, C.},
  year={2020},
  url={https://cran.r-project.org/web/packages/tmle/tmle.pdf}
}

@manual{coyle2021sl3,
  author = {Coyle, Jeremy R and Hejazi, Nima S and Malenica, Ivana and
  Sofrygin, Oleg},
  title = {{sl3}: Modern Pipelines for Machine Learning and {Super
    Learning}},
  year = {2021},
  howpublished = {\url{https://github.com/tlverse/sl3}},
  note = {{R} package version 1.4.2},
  url = {https://doi.org/10.5281/zenodo.1342293},
  doi = {10.5281/zenodo.1342293}
}

@book{coyle2021tlverse,
  author = {Coyle, Jeremy R and Hejazi, Nima S and Melencia, Ivana and Phillips, Rachael and Hubbard, Alex},
  title = {Targeted Learning in {R}: Causal Data Science with the {tlverse} Software Ecosystem},
  year = {2021},
  howpublished = {\url{https://github.com/tlverse/tlverse-handbook}},
  url = {https://tlverse.org/tlverse-handbook/}
}

@Article{Zhong2021AIPW,
  title = {AIPW: An R Package for Augmented Inverse Probability
    Weighted Estimation of Average Causal Effects},
  author = {{Yongqi Zhong} and {Edward H. Kennedy} and {Lisa M.
    Bodnar} and {Ashley I. Naimi}},
  journal = {American Journal of Epidemiology},
  year = {2021},
  note = {In Press},
}

@Article{Lendle2017ltmle,
  title = {{ltmle}: An {R} Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data},
  author = {Lendle, Samuel D. and Schwab, Joshua and Petersen, Maya L. and van der Laan, Mark J.},
  journal = {Journal of Statistical Software},
  year = {2017},
  volume = {81},
  number = {1},
  pages = {1-21},
  doi = {10.18637/jss.v081.i01}
}

@software{coyle2021tmle3-rpkg,
  author = {Coyle, Jeremy R},
  title = {{tmle3}: The Extensible {TMLE} Framework},
  year = {2021},
  howpublished = {\url{https://github.com/tlverse/tmle3}},
  note = {{R} package version 0.2.0},
  url = {https://doi.org/10.5281/zenodo.4603358},
  doi = {10.5281/zenodo.4603358}
}