@article{sjolander2018estimation,
  title={Estimation of causal effect measures with the R-package stdReg},
  author={Sj{\"o}lander, Arvid},
  journal={European journal of epidemiology},
  volume={33},
  pages={847--858},
  year={2018},
  publisher={Springer}
}

@article{sjolander2016regression,
  title={Regression standardization with the R package stdReg},
  author={Sj{\"o}lander, Arvid},
  journal={European journal of epidemiology},
  volume={31},
  pages={563--574},
  year={2016},
  publisher={Springer}
}

@article{witte2019covariate,
  title={Covariate selection strategies for causal inference: Classification and comparison},
  author={Witte, Janine and Didelez, Vanessa},
  journal={Biometrical Journal},
  volume={61},
  number={5},
  pages={1270--1289},
  year={2019},
  publisher={Wiley Online Library}
}

@article{gabriel2023inverse,
  title={Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation},
  author={Gabriel, Erin E and Sachs, Michael C and Martinussen, Torben and Waernbaum, Ingeborg and Goetghebeur, Els and Vansteelandt, Stijn and Sj{\"o}lander, Arvid},
  journal={Statistics in Medicine},
  volume={43},
  number={3},
  pages={534--547},
  year={2024},
  publisher={Wiley Online Library}
}


@article{MartinussenHR20,
author={Martinussen, T. and Vansteelandt, S. and Andersen, P.K.},
title={Subtleties in the interpretation of hazard contrasts},
journal={Lifetime Data Analysis},
volume={26},
pages={833–855},
year={2020}
}

@article{chen2001causal,
  title={Causal inference on the difference of the restricted mean lifetime between two groups},
  author={Chen, Pei-Yun and Tsiatis, Anastasios A},
  journal={Biometrics},
  volume={57},
  number={4},
  pages={1030--1038},
  year={2001},
  publisher={Wiley Online Library}
}

@article{wickham2014tidy,
  title={Tidy data},
  author={Wickham, Hadley},
  journal={Journal of statistical software},
  volume={59},
  pages={1--23},
  year={2014}
}

@article{hernan2010hazards,
  title={The hazards of hazard ratios},
  author={Hern{\'a}n, Miguel A},
  journal={Epidemiology},
  volume={21},
  number={1},
  pages={13--15},
  year={2010},
  publisher={LWW}
}

@article{stefanski2002calculus,
  title={The calculus of M-estimation},
  author={Stefanski, Leonard A and Boos, Dennis D},
  journal={The American Statistician},
  volume={56},
  number={1},
  pages={29--38},
  year={2002},
  publisher={Taylor \& Francis}
}

@article{tibshirani1993introduction,
  title={An introduction to the bootstrap},
  author={Tibshirani, Robert J and Efron, Bradley},
  journal={Monographs on statistics and applied probability},
  volume={57},
  number={1},
  pages={1--436},
  year={1993}
}

@article{robins1986new,
  title={A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect},
  author={Robins, James},
  journal={Mathematical modelling},
  volume={7},
  number={9-12},
  pages={1393--1512},
  year={1986},
  publisher={Elsevier}
}

@article{MCGRATH2020100008,
title = {gfoRmula: An R Package for Estimating the Effects of Sustained Treatment Strategies via the Parametric g-formula},
journal = {Patterns},
volume = {1},
number = {3},
pages = {100008},
year = {2020},
issn = {2666-3899},
doi = {https://doi.org/10.1016/j.patter.2020.100008},
url = {https://www.sciencedirect.com/science/article/pii/S2666389920300088},
author = {Sean McGrath and Victoria Lin and Zilu Zhang and Lucia C. Petito and Roger W. Logan and Miguel A. Hernán and Jessica G. Young},
keywords = {g-formula, longitudinal data, causal inference, R},
}

@article{naimi2021challenges,
    author = {Naimi, Ashley I and Mishler, Alan E and Kennedy, Edward H},
    title = {Challenges in Obtaining Valid Causal Effect Estimates With Machine Learning Algorithms},
    journal = {American Journal of Epidemiology},
    volume = {192},
    number = {9},
    pages = {1536-1544},
    year = {2021},
    month = {07},
    abstract = {Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data-generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithms can perform worse than parametric regression. We demonstrate the performance of ML-based singly and doubly robust estimators. We used 100 Monte Carlo samples with sample sizes of 200, 1,200, and 5,000 to investigate bias and confidence-interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to singly robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Doubly robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML-based singly robust methods should be avoided.},
    issn = {0002-9262},
    doi = {10.1093/aje/kwab201},
    url = {https://doi.org/10.1093/aje/kwab201},
    eprint = {https://academic.oup.com/aje/article-pdf/192/9/1536/51319526/kwab201.pdf},
}

@article{zivich2021machine,
author = {Zivich,Paul N. and Breskin,Alexander},
title = {Machine learning for causal inference: on the use of cross-fit estimators.},
year = {2021},
volume = {32},
pages = {393–401},
publisher = {Lippincott Williams & Wilkins, Inc.},
address = {Hagerstown},
language ={English},
item-type = {Journal article},
DOI = {10.1097/EDE.0000000000001332},
ISSN = {1531-5487 1044-3983} ,
issue = {3}
}



