@incollection{diaz2018stochastic,
  title={Stochastic Treatment Regimes},
  author={D{\'\i}az, Iv{\'a}n and {van der Laan}, Mark J},
  booktitle={Targeted Learning in Data Science: Causal Inference for Complex
    Longitudinal Studies},
  pages={167--180},
  year={2018},
  publisher={Springer},
  doi = {10.1007/978-3-319-65304-4_14},
  url = {https://doi.org/10.1007/978-3-319-65304-4_14}
}

@article{diaz2012population,
  title={Population intervention causal effects based on stochastic
    interventions},
  author={D{\'\i}az, Iv{\'a}n and {van der Laan}, Mark J},
  journal={Biometrics},
  volume={68},
  number={2},
  pages={541--549},
  year={2012},
  publisher={Wiley Online Library},
  doi = {10.1111/j.1541-0420.2011.01685.x},
  url = {https://doi.org/10.1111/j.1541-0420.2011.01685.x}

}

@book{vdl2018targeted,
  title={Targeted Learning in Data Science: Causal Inference for Complex
    Longitudinal Studies},
  author={{van der Laan}, Mark J and Rose, Sherri},
  year={2018},
  publisher={Springer}
}

@book{vdl2011targeted,
  title={Targeted Learning: Causal Inference for Observational and Experimental
    Data},
  author={{van der Laan}, Mark J and Rose, Sherri},
  year={2011},
  publisher={Springer}
}

@article{diaz2011super,
  title={Super learner based conditional density estimation with application to
    marginal structural models},
  author={D{\'\i}az, Iv{\'a}n and {van der Laan}, Mark J},
  journal={International Journal of Biostatistics},
  volume={7},
  number={1},
  pages={1--20},
  year={2011},
  publisher={De Gruyter},
  doi = {10.2202/1557-4679.1356},
  url = {https://doi.org/10.2202/1557-4679.1356}
}

@article{diaz2020causal,
  title={Causal mediation analysis for stochastic interventions},
  author={D{\'\i}az, Iv{\'a}n and Hejazi, Nima S},
  year={2020},
  url = {https://doi.org/10.1111/rssb.12362},
  doi = {10.1111/rssb.12362},
  journal={Journal of the Royal Statistical Society: Series B (Statistical
    Methodology)},
  volume = {82},
  number = {3},
  pages = {661-683},
  publisher={Wiley Online Library}
}

@incollection{vdl2018highly,
  title={Highly Adaptive Lasso {(HAL)}},
  author={{van der Laan}, Mark J and Benkeser, David},
  booktitle={Targeted Learning in Data Science: Causal Inference for Complex
    Longitudinal Studies},
  pages={77--94},
  year={2018},
  publisher={Springer},
  doi = {10.1007/978-3-319-65304-4_6},
  url = {https://doi.org/10.1007/978-3-319-65304-4_6}
}

@article{vdl2017generally,
  title={A generally efficient targeted minimum loss-based estimator based on
    the highly adaptive lasso},
  author={{van der Laan}, Mark J},
  journal={International Journal of Biostatistics},
  volume={13},
  number={2},
  year={2017},
  doi = {10.1515/ijb-2015-0097},
  url = {https://doi.org/10.1515/ijb-2015-0097},
  publisher = {De Gruyter}
}

@manual{coyle2022hal9001-rpkg,
  author = {Coyle, Jeremy R and Hejazi, Nima S and Phillips, Rachael V and
    {van der Laan}, Lars WP and {van der Laan}, Mark J},
  title = {{hal9001}: The scalable highly adaptive lasso},
  year  = {2022},
  url = {https://CRAN.R-project.org/package=hal9001},
  doi = {10.5281/zenodo.3558313},
  note = {{R} package version 0.4.2}
}

@article{hejazi2020hal9001-joss,
  author = {Hejazi, Nima S and Coyle, Jeremy R and {van der Laan}, Mark J},
  title = {{hal9001}: Scalable highly adaptive lasso regression in {R}},
  year  = {2020},
  url = {https://doi.org/10.21105/joss.02526},
  doi = {10.21105/joss.02526},
  journal = {Journal of Open Source Software},
  publisher = {The Open Journal}
}

@inproceedings{benkeser2016highly,
  title={The highly adaptive lasso estimator},
  author={Benkeser, David and {van der Laan}, Mark J},
  booktitle={Proceedings of the International Conference on Data Science and
    Advanced Analytics},
  volume={2016},
  pages={689},
  year={2016},
  organization={NIH Public Access},
  doi = {10.1109/dsaa.2016.93},
  url = {https://doi.org/10.1109/dsaa.2016.93}
}

@article{hirano2004propensity,
  title={The propensity score with continuous treatments},
  author={Hirano, Keisuke and Imbens, Guido W},
  journal={Applied Bayesian Modeling and Causal Inference from Incomplete-data
    Perspectives},
  volume={226164},
  pages={73--84},
  year={2004}
}

@article{zhu2015boosting,
  title={A boosting algorithm for estimating generalized propensity scores with
    continuous treatments},
  author={Zhu, Yeying and Coffman, Donna L and Ghosh, Debashis},
  journal={Journal of Causal Inference},
  volume={3},
  number={1},
  pages={25--40},
  year={2015},
  publisher={De Gruyter},
  doi = {10.1515/jci-2014-0022},
  url = {https://doi.org/10.1515/jci-2014-0022}
}

@article{imai2004causal,
  title={Causal inference with general treatment regimes: Generalizing the
    propensity score},
  author={Imai, Kosuke and Van Dyk, David A},
  journal={Journal of the American Statistical Association},
  volume={99},
  number={467},
  pages={854--866},
  year={2004},
  publisher={Taylor \& Francis}
}

@article{ertefaie2020nonparametric,
  doi = {10.1111/biom.13719},
  url = {https://doi.org/10.1111/biom.13719},
  year = {2022},
  publisher = {},
  journal = {Biometrics},
  volume = {(in press)},
  number = {},
  pages = {},
  author = {Ertefaie, Ashkan and Hejazi, Nima S and {van der Laan}, Mark J},
  title = {Nonparametric inverse-probability-weighted estimators based on the
    highly adaptive lasso}
}

@article{hejazi2022efficient,
  doi = {},
  url = {https://arxiv.org/abs/2205.05777},
  year = {2022},
  publisher = {},
  journal = {arXiv},
  volume = {},
  number = {},
  pages = {},
  author = {Hejazi, Nima S and Benkeser, David and D{\'\i}az, Iv{\'a}n and
    {van der Laan}, Mark J},
  title = {Efficient estimation of modified treatment policy effects based on
    the generalized propensity score}
}

@article{bang2005doubly,
  Author = {Bang, Heejung and Robins, James M},
  Journal = {Biometrics},
  Number = {4},
  Pages = {962--973},
  Publisher = {Wiley Online Library},
  Title = {Doubly robust estimation in missing data and causal inference
    models},
  Volume = {61},
  Year = {2005},
  doi = {10.1111/j.1541-0420.2005.00377.x},
  url = {https://doi.org/10.1111/j.1541-0420.2005.00377.x}
}

@article{takeuchi2009nonparametric,
  title={Nonparametric conditional density estimation using piecewise-linear
    solution path of kernel quantile regression},
  author={Takeuchi, Ichiro and Nomura, Kaname and Kanamori, Takafumi},
  journal={Neural Computation},
  volume={21},
  number={2},
  pages={533--559},
  year={2009},
  publisher={MIT Press},
  doi = {10.1162/neco.2008.10-07-628},
  url = {https://doi.org/10.1162/neco.2008.10-07-628}
}

@article{bashtannyk2001bandwidth,
  title={Bandwidth selection for kernel conditional density estimation},
  author={Bashtannyk, David M and Hyndman, Rob J},
  journal={Computational Statistics \& Data Analysis},
  volume={36},
  number={3},
  pages={279--298},
  year={2001},
  publisher={Elsevier},
  doi = {10.1016/s0167-9473(00)00046-3},
  url = {https://doi.org/10.1016/s0167-9473(00)00046-3}
}

@inproceedings{neuneier1994estimation,
  title={Estimation of conditional densities: A comparison of neural network
    approaches},
  author={Neuneier, R and Hergert, F and Finnoff, W and Ormoneit, D},
  booktitle={International Conference on Artificial Neural Networks},
  pages={689--692},
  year={1994},
  organization={Springer},
  doi = {10.1007/978-1-4471-2097-1_162},
  url = {https://doi.org/10.1007/978-1-4471-2097-1_162}
}

@article{tibshirani1996regression,
  title={Regression shrinkage and selection via the lasso},
  author={Tibshirani, Robert},
  journal={Journal of the Royal Statistical Society: Series B
    (Methodological)},
  volume={58},
  number={1},
  pages={267--288},
  year={1996},
  publisher={Wiley Online Library}
}

@manual{R,
  address = {Vienna, Austria},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  title = {\text{R}: A Language and Environment for Statistical Computing},
  url = {https://www.R-project.org/},
  year = {2022}
}

@article{coyle2018origami,
  author = {Coyle, Jeremy R and Hejazi, Nima S},
  title = {{origami}: A Generalized Framework for Cross-Validation in {R}},
  journal = {The Journal of Open Source Software},
  volume = {3},
  number = {21},
  month = {January},
  year  = {2018},
  publisher = {The Open Journal},
  doi = {10.21105/joss.00512},
  url = {https://doi.org/10.21105/joss.00512}
}

@techreport{vdl2015generally,
  title={A generally efficient targeted minimum loss based estimator},
  author={{van der Laan}, Mark J},
  institution={University of California, Berkeley},
  note={UC Berkeley Division of Biostatistics Working Paper Series},
  year={2015},
  month={December},
  number={343},
  url={https://biostats.bepress.com/ucbbiostat/paper343/}
}

@article{vdl2004asymptotic,
  title={Asymptotic optimality of likelihood-based cross-validation},
  author={{van der Laan}, Mark J and Dudoit, Sandrine and Keles, Sunduz},
  journal={Statistical Applications in Genetics and Molecular Biology},
  volume={3},
  number={1},
  pages={1--23},
  year={2004},
  doi = {10.2202/1544-6115.1036},
  url = {https://doi.org/10.2202/1544-6115.1036}
}

@article{dudoit2005asymptotics,
  title={Asymptotics of cross-validated risk estimation in estimator selection
    and performance assessment},
  author={Dudoit, Sandrine and {van der Laan}, Mark J},
  journal={Statistical Methodology},
  volume={2},
  number={2},
  pages={131--154},
  year={2005},
  publisher={Elsevier},
  doi = {10.1016/j.stamet.2005.02.003},
  url = {https://doi.org/10.1016/j.stamet.2005.02.003}
}

@article{bibaut2019fast,
  title={Fast rates for empirical risk minimization with cadlag losses with
    bounded sectional variation norm},
  author={Bibaut, Aur{\'e}lien F and van der Laan, Mark J},
  journal={arXiv preprint arXiv:1907.09244},
  url = {https://arxiv.org/abs/1907.09244},
  year={2019}
}

@article{vdl2019efficient,
  title={Efficient estimation of pathwise differentiable target parameters with
    the undersmoothed highly adaptive lasso},
  author={{van der Laan}, Mark J and Benkeser, David and Cai, Weixin},
  journal={International Journal of Biostatistics},
  doi = {10.1515/ijb-2019-0092},
  url = {https://doi.org/10.1515/ijb-2019-0092},
  year={2022},
  volume = {(in press)},
  number = {},
  pages = {},
  publisher = {De Gruyter}
}

@article{vdl2017uniform,
  author = {{van der Laan}, Mark J and Bibaut, Aur{\'e}lien F},
  journal = {arXiv preprint arXiv:1709.06256},
  title = {Uniform Consistency of the Highly Adaptive Lasso Estimator of
    Infinite-Dimensional Parameters},
  year = {2017}
}

@article{hejazi2020efficient,
  author = {Hejazi, Nima S and {van der Laan}, Mark J and Janes, Holly E and
    Gilbert, Peter B and Benkeser, David C},
  title = {Efficient nonparametric inference on the effects of stochastic
    interventions under two-phase sampling, with applications to vaccine
    efficacy trials},
  year = {2020},
  doi = {10.1111/biom.13375},
  url = {https://doi.org/10.1111/biom.13375},
  journal = {Biometrics},
  volume = {77},
  number = {4},
  pages = {1241--1253},
  publisher = {Wiley Online Library}
}

@article{haneuse2013estimation,
  title={Estimation of the effect of interventions that modify the received
    treatment},
  author={Haneuse, Sebastian and Rotnitzky, A},
  journal={Statistics in Medicine},
  volume={32},
  number={30},
  pages={5260--5277},
  year={2013},
  publisher={Wiley Online Library},
  doi = {10.1002/sim.5907},
  url = {https://doi.org/10.1002/sim.5907}
}

@article{rosenbaum1983central,
  title={The central role of the propensity score in observational studies for
    causal effects},
  author={Rosenbaum, Paul R and Rubin, Donald B},
  journal={Biometrika},
  volume={70},
  number={1},
  pages={41--55},
  year={1983},
  publisher={Biometrika Trust},
  doi = {10.1017/cbo9780511810725.016},
  url = {https://doi.org/10.1017/cbo9780511810725.016}
}

@techreport{vdl2010targeted,
  title={Targeted maximum likelihood learning based causal inference},
  author={{van der Laan}, Mark J},
  institution={University of California, Berkeley},
  note={UC Berkeley Division of Biostatistics Working Paper Series},
  year={2010},
  month={February},
  number={259},
  url={https://biostats.bepress.com/ucbbiostat/paper259},
  publisher={bepress}
}

@article{cheng2004semiparametric,
  title={Semiparametric density estimation under a two-sample density ratio
    model},
  author={Cheng, Kuang Fu and Chu, Chih-Kang},
  journal={Bernoulli},
  volume={10},
  number={4},
  pages={583--604},
  year={2004},
  publisher={Bernoulli Society for Mathematical Statistics and Probability},
  doi = {10.3150/bj/1093265631},
  url = {https://doi.org/10.3150/bj/1093265631}
}

@article{qin1998inferences,
  title={Inferences for case-control and semiparametric two-sample density
    ratio models},
  author={Qin, Jing},
  journal={Biometrika},
  volume={85},
  number={3},
  pages={619--630},
  year={1998},
  publisher={Oxford University Press},
  doi = {10.1093/biomet/85.3.619},
  url = {https://doi.org/10.1093/biomet/85.3.619}
}

@book{sugiyama2012density,
  title={Density Ratio Estimation in Machine Learning},
  author={Sugiyama, Masashi and Suzuki, Taiji and Kanamori, Takafumi},
  year={2012},
  publisher={Cambridge University Press}
}

@article{vdl2007super,
  title={{Super Learner}},
  author={{van der Laan}, Mark J and Polley, Eric C and Hubbard, Alan E},
  journal={Statistical Applications in Genetics and Molecular Biology},
  volume={6},
  number={1},
  year={2007}
}

@article{breiman1996stacked,
  title={Stacked regressions},
  author={Breiman, Leo},
  journal={Machine Learning},
  volume={24},
  number={1},
  pages={49--64},
  year={1996},
  publisher={Springer},
  doi = {10.1007/bf00117832},
  url = {https://doi.org/10.1007/bf00117832}
}

@article{wolpert1992stacked,
  title={Stacked generalization},
  author={Wolpert, David H},
  journal={Neural Networks},
  volume={5},
  number={2},
  pages={241--259},
  year={1992},
  publisher={Elsevier}
}

@inproceedings{sugiyama2010conditional,
  title={Conditional density estimation via least-squares density ratio
    estimation},
  author={Sugiyama, Masashi and Takeuchi, Ichiro and Suzuki, Taiji and
    Kanamori, Takafumi and Hachiya, Hirotaka and Okanohara, Daisuke},
  booktitle={Proceedings of the Thirteenth International Conference on
    Artificial Intelligence and Statistics},
  pages={781--788},
  year={2010},
  organization={{JMLR Workshop and Conference Proceedings}}
}

@manual{hejazi2022txshift-rpkg,
  author = {Hejazi, Nima S and Benkeser, David C},
  title = {{\texttt{txshift}}: Efficient Estimation of the Causal Effects of
    Stochastic Interventions},
  year  = {2022},
  doi = {10.5281/zenodo.4070042},
  url = {https://github.com/nhejazi/txshift},
  note = {R package version 0.3.8}
}

@article{hejazi2020txshift-joss,
  doi = {10.21105/joss.02447},
  year  = {2020},
  month = {10},
  publisher = {The Open Journal},
  journal = {Journal of Open Source Software},
  volume = {5},
  number = {54},
  pages = {2447},
  author = {Hejazi, Nima S and Benkeser, David C},
  title = {{txshift}: Efficient estimation of the causal effects of stochastic
    interventions in {R}}
}

@article{vdl2006targeted,
  title={Targeted maximum likelihood learning},
  author={{van der Laan}, Mark J and Rubin, Daniel},
  journal={International Journal of Biostatistics},
  volume={2},
  number={1},
  year={2006},
  doi = {10.2202/1557-4679.1043},
  url = {https://doi.org/10.2202/1557-4679.1043}
}
