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}

@article{backhoff2020all,
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}

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}


@article{barbie2014topology,
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@misc{byrd2019what,
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}

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@article{chernozhukov2018double,
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}

@article{chickering2006incompatibility,
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@inproceedings{correa2017,
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}

@inproceedings{correa2018,
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  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
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}

@inproceedings{correa2019,
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  number    = {01},
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  year      = {2019}
}

@incollection{cortes2008sample,
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  langid    = {english}
}

@inproceedings{coston2020counterfactual,
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@article{dempster1977maximum,
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@article{ditlhong2018comparative,
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}

@misc{du2021fair,
  title         = {Fair {{Regression}} under {{Sample Selection Bias}}},
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  eprinttype    = {arxiv},
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}

@inproceedings{duchon1977splines,
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}

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  year    = {2011}
}

@misc{dudik2011doubly,
  title         = {Doubly {{Robust Policy Evaluation}} and {{Learning}}},
  author        = {Dudik, Miroslav and Langford, John and Li, Lihong},
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  month         = may,
  number        = {arXiv:1103.4601},
  eprint        = {1103.4601},
  eprinttype    = {arxiv},
  primaryclass  = {cs, stat},
  publisher     = {{arXiv}},
  doi           = {10.48550/arXiv.1103.4601},
  url           = {http://arxiv.org/abs/1103.4601},
  archiveprefix = {arXiv},
  keywords      = {Estimation,IPW,Reading list}
}

@article{dudik2014doubly,
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  year          = {2014},
  month         = nov,
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}

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  address    = {{New York}},
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  lccn       = {HA29 .E497 2010},
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}

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}

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@article{goel2021importance,
  title     = {The {{Importance}} of {{Modeling Data Missingness}} in {{Algorithmic Fairness}}: {{A Causal Perspective}}},
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  url       = {https://ojs.aaai.org/index.php/AAAI/article/view/16926},
  copyright = {Copyright (c) 2021 Association for the Advancement of Artificial Intelligence},
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  keywords  = {Causality}
}

@article{graham2003adding,
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}

@misc{guerdan2023ground,
  title         = {Ground(Less) {{Truth}}: {{A Causal Framework}} for {{Proxy Labels}} in {{Human-Algorithm Decision-Making}}},
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  year          = {2023},
  month         = feb,
  number        = {arXiv:2302.06503},
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  archiveprefix = {arxiv},
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}

@article{harrison1978hedonic,
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@article{heckman1979sample,
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}

@book{heij2004econometric,
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@book{hernan2021causal,
  title     = {Causal Inference},
  author    = {Hern{\'a}n, Miguel and Robins, James M.},
  year      = {2021},
  series    = {Chapman \& {{Hall}}/{{CRC}} Monographs on Statistics \& Applied Probability},
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  address   = {{Boca Raton}},
  isbn      = {978-1-4200-7616-5},
  langid    = {english}
}

@article{heymann2021,
  title   = {Causal inference theory with information dependency models},
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  journal = {arXiv preprint arXiv:2108.03099},
  year    = {2021}
}

@article{horvitz1952generalization,
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  issn      = {0162-1459},
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  url       = {https://www.jstor.org/stable/2280784},
  keywords  = {IPW,Reading list}
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@article{jakobsen2017when,
  title    = {When and How Should Multiple Imputation Be Used for Handling Missing Data in Randomised Clinical Trials \textendash{} a Practical Guide with Flowcharts},
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  keywords = {Missing data,Multiple imputation,Randomised clinical trials}
}

@article{jin2022modular,
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}

@inproceedings{jung2021estimating,
  title     = {Estimating {{Identifiable Causal Effects}} on {{Markov Equivalence Class}} through {{Double Machine Learning}}},
  booktitle = {Proceedings of the 38th {{International Conference}} on {{Machine Learning}}},
  author    = {Jung, Yonghan and Tian, Jin and Bareinboim, Elias},
  year      = {2021},
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  pages     = {5168--5179},
  publisher = {{PMLR}},
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  url       = {https://proceedings.mlr.press/v139/jung21b.html},
  langid    = {english}
}

@article{kalisch2012causal,
  title   = {Causal Inference Using Graphical Models with the {R}
             Package {pcalg}},
  author  = {Markus Kalisch and Martin M\"achler and Diego Colombo and
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  journal = {Journal of Statistical Software},
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  doi     = {10.18637/jss.v047.i11}
}

@article{kang2007demystifying,
  title         = {Demystifying {{Double Robustness}}: {{A Comparison}} of {{Alternative Strategies}} for {{Estimating}} a {{Population Mean}} from {{Incomplete Data}}},
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  year          = {2007},
  month         = nov,
  journal       = {Statistical Science},
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  archiveprefix = {arXiv},
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@book{kechris2012classical,
  title     = {Classical descriptive set theory},
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  publisher = {Springer}
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@misc{klusowski2021universal,
  title         = {Universal {{Consistency}} of {{Decision Trees}} in {{High Dimensions}}},
  author        = {Klusowski, Jason M.},
  year          = {2021},
  month         = jun,
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  eprinttype    = {arxiv},
  primaryclass  = {cs, math, stat},
  publisher     = {{arXiv}},
  doi           = {10.48550/arXiv.2104.13881},
  url           = {http://arxiv.org/abs/2104.13881},
  archiveprefix = {arXiv},
  keywords      = {Computer Science - Machine Learning,Mathematics - Statistics Theory,Statistics - Machine Learning}
}

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}

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@inproceedings{lee2020,
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}

@article{leecausal,
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@book{little2019statistical,
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@article{little2020algorithmic,
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  doi     = {10.5705/ss.202018.0207},
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  langid  = {english}
}

@article{little2021missing,
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  langid  = {english}
}

@article{loh2019classification,
  title     = {Classification and {{Regression Trees}} and {{Forests}} for {{Incomplete Data}} from {{Sample Surveys}}},
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@misc{martius2016extrapolation,
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  eprint        = {1610.02995},
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  archiveprefix = {arxiv},
  keywords      = {{68T05, 68T30, 68T40, 62J02, 65D15},Computer Science - Artificial Intelligence,Computer Science - Machine Learning,I.2.6,I.2.8}
}

@article{mealli2015clarifying,
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@article{miao2018,
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@article{mohan2021graphical,
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  langid  = {english}
}

@article{mooij2016distinguishing,
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@inproceedings{nabi2020full,
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  langid    = {english}
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@book{pearl2009causality,
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@article{raghunathan2000multivariate,
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@article{robins1995semiparametric,
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  langid    = {english}
}

@article{rosenbaum1984reducing,
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@article{rotnitzky1998semiparametric,
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}

@article{rubin1976inference,
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}

@book{rubin1987multiple,
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  langid    = {english}
}

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@article{scharfstein1999adjusting,
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@inproceedings{scholkopf2012causal,
  title     = {On Causal and Anticausal Learning},
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  isbn      = {978-1-4503-1285-1}
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@article{seaman2012combining,
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  year       = {2012},
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  langid     = {english},
  keywords   = {1958 British Birth Cohort,Marginal model,Missing at random,Survey weighting},
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}

@article{seaman2013review,
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  langid  = {english}
}

@article{shimodaira2000improving,
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}

@inproceedings{shpitser2006,
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}

@article{sugiyama2007covariate,
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@article{thoemmes2015graphical,
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@inproceedings{tian2002,
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@book{tsiatis2006semiparametric,
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  author    = {Tsiatis, Anastasios A.},
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  publisher = {{Springer}},
  address   = {{New York}},
  isbn      = {978-0-387-32448-7},
  langid    = {english},
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@book{vanbuuren2018flexible,
  title  = {Flexible {{Imputation}} of {{Missing Data}}},
  author = {{van Buuren}, Stef},
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@book{vanderlaan2003unified,
  title     = {Unified {{Methods}} for {{Censored Longitudinal Data}} and {{Causality}}},
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  year      = {2003},
  series    = {Springer {{Series}} in {{Statistics}}},
  publisher = {{Springer New York}},
  address   = {{New York, NY}},
  doi       = {10.1007/978-0-387-21700-0},
  url       = {http://link.springer.com/10.1007/978-0-387-21700-0},
  isbn      = {978-1-4419-3055-2 978-0-387-21700-0},
  langid    = {english}
}
@article{vapnik2009new,
  title    = {A New Learning Paradigm: {{Learning}} Using Privileged Information},
  author   = {Vapnik, Vladimir and Vashist, Akshay},
  year     = {2009},
  month    = jul,
  journal  = {Neural Networks},
  series   = {Advances in {{Neural Networks Research}}: {{IJCNN2009}}},
  volume   = {22},
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  issn     = {0893-6080},
  doi      = {10.1016/j.neunet.2009.06.042},
  url      = {https://www.sciencedirect.com/science/article/pii/S0893608009001130},
  langid   = {english},
  keywords = {Hidden information,Learning with teacher,Machine learning,Oracle SVM,Privileged information,SVM,SVM+}
}

@article{vapnik2015learning,
  title   = {Learning {{Using Privileged Information}}: {{Similarity Control}} and {{Knowledge Transfer}}},
  author  = {Vapnik, Vladimir and Izmailov, Rauf},
  year    = {2015},
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}

@inproceedings{weifan2005improved,
  title     = {An {{Improved Categorization}} of {{Classifier}}'s {{Sensitivity}} on {{Sample Selection Bias}}},
  booktitle = {Fifth {{IEEE International Conference}} on {{Data Mining}} ({{ICDM}}'05)},
  author    = {{Wei Fan} and Davidson, I. and Zadrozny, B. and {Philip S. Yu}},
  year      = {2005},
  pages     = {605--608},
  publisher = {{IEEE}},
  address   = {{Houston, TX, USA}},
  doi       = {10.1109/ICDM.2005.24},
  url       = {http://ieeexplore.ieee.org/document/1565737/},
  isbn      = {978-0-7695-2278-4},
  langid    = {english}
}

@article{witte2020,
  title     = {On efficient adjustment in causal graphs},
  author    = {Witte, Janine and Henckel, Leonard and Maathuis, Marloes H and Didelez, Vanessa},
  journal   = {Journal of Machine Learning Research},
  volume    = {21},
  pages     = {246},
  year      = {2020},
  publisher = {MIT Press}
}

@article{wood2003thin,
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  author     = {Wood, Simon N.},
  year       = {2003},
  journal    = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume     = {65},
  number     = {1},
  pages      = {95--114},
  issn       = {1467-9868},
  doi        = {10.1111/1467-9868.00374},
  url        = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00374},
  langid     = {english},
  keywords   = {Generalized additive model,Regression spline,Thin plate spline},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00374}
}


@article{wood2015package,
  title   = {Package ‘mgcv’},
  author  = {Wood, Simon},
  journal = {R package version},
  volume  = {1},
  number  = {29},
  pages   = {729},
  year    = {2015}
}

@book{wood2017generalized,
  title     = {Generalized {{Additive Models}}},
  author    = {Wood, Simon N},
  year      = {2017},
  publisher = {{Chapman and Hall/CRC}},
  langid    = {english}
}

@misc{xu2021understandinga,
  title         = {Understanding the Role of Importance Weighting for Deep Learning},
  author        = {Xu, Da and Ye, Yuting and Ruan, Chuanwei},
  year          = {2021},
  month         = mar,
  number        = {arXiv:2103.15209},
  eprint        = {2103.15209},
  eprinttype    = {arxiv},
  primaryclass  = {cs},
  publisher     = {{arXiv}},
  doi           = {10.48550/arXiv.2103.15209},
  url           = {http://arxiv.org/abs/2103.15209},
  archiveprefix = {arXiv},
  keywords      = {Computer Science - Machine Learning}
}

@inproceedings{zadrozny2003cost,
  title        = {Cost-sensitive learning by cost-proportionate example weighting},
  author       = {Zadrozny, Bianca and Langford, John and Abe, Naoki},
  booktitle    = {Third IEEE international conference on data mining},
  pages        = {435--442},
  year         = {2003},
  organization = {IEEE}
}

@inproceedings{zadrozny2004learning,
  title     = {Learning and evaluating classifiers under sample selection bias},
  author    = {Zadrozny, Bianca},
  booktitle = {Proceedings of the twenty-first international conference on Machine learning},
  pages     = {114},
  year      = {2004}
}

@inproceedings{zhang2013domain,
  title     = {Domain {{Adaptation}} under {{Target}} and {{Conditional Shift}}},
  booktitle = {Proceedings of the 30th {{International Conference}} on {{Machine Learning}}},
  author    = {Zhang, Kun and Sch{\"o}lkopf, Bernhard and Muandet, Krikamol and Wang, Zhikun},
  year      = {2013},
  month     = may,
  pages     = {819--827},
  publisher = {{PMLR}},
  issn      = {1938-7228},
  url       = {https://proceedings.mlr.press/v28/zhang13d.html},
  langid    = {english}
}

@article{zhou2010blockconditional,
  title     = {Block-{{Conditional Missing}} at {{Random Models}} for {{Missing Data}}},
  author    = {Zhou, Yan and Little, Roderick J. A. and Kalbfleisch, John D.},
  year      = {2010},
  journal   = {Statistical Science},
  volume    = {25},
  number    = {4},
  pages     = {517--532},
  publisher = {{Institute of Mathematical Statistics}},
  issn      = {0883-4237},
  url       = {https://www.jstor.org/stable/23061099}
}
