biblio.bib

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@inproceedings{Bareinboim2014:selectionbias,
  title = {Recovering from Selection Bias in Causal and Statistical Inference},
  author = {Bareinboim, E. and Tian, J. and Pearl, J.},
  booktitle = {Proceedings of the 28th AAAI Conference on Neural Information Processing Systems},
  year = {2014}
}

@inproceedings{Shpitser2015,
  title = {Missing Data as a Causal and Probabilistic Problem},
  author = {Shpitser, I. and Mohan, K. and Pearl, J.},
  editor = {Marina Meila and Tom Heskes},
  booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence},
  publisher = {AUAI Press},
  pages = {802--811},
  year = {2015}
}

@inproceedings{Mohan2013,
author = {K. Mohan and J. Pearl and J. Tian},
title = {Graphical models for inference with missing data},
booktitle = {Advances in Neural Information Systems},
volume = {26},
pages = {1277--1285},
year = {2013}
}

@incollection{smr1999,
author={Peter Spirtes and Christopher Meek and Thomas Richardson},
title={An Algorithm for Causal Inference in the Presence of Latent Variables and Selection Bias},
booktitle={Computation, Causation \& Discovery},
editor = {Clark Glymour and Gregory F. Cooper},
year = {1999},
pages = {211--252},
publisher = { AAAI / MIT Press}
}

@article{holland1986statistics,
  title={Statistics and causal inference},
  author={Holland, Paul W},
  journal={Journal of the American statistical Association},
  volume={81},
  number={396},
  pages={945--960},
  year={1986},
  publisher={Taylor \& Francis}
}

@inproceedings{kallus2018confounding,
  title={Confounding-robust policy improvement},
  author={Kallus, Nathan and Zhou, Angela},
  booktitle={Advances in neural information processing systems},
  pages={9269--9279},
  year={2018}
}

@inproceedings{DBLP:conf/icml/ThomasB16,
  author    = {Philip S. Thomas and
               Emma Brunskill},
  editor    = {Maria{-}Florina Balcan and
               Kilian Q. Weinberger},
  title     = {Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning},
  booktitle = {Proceedings of the 33nd International Conference on Machine Learning,
               {ICML} 2016, New York City, NY, USA, June 19-24, 2016},
  series    = {{JMLR} Workshop and Conference Proceedings},
  volume    = {48},
  pages     = {2139--2148},
  publisher = {JMLR.org},
  year      = {2016},
  OPTurl       = {http://proceedings.mlr.press/v48/thomasa16.html},
  OPTtimestamp = {Wed, 29 May 2019 08:41:46 +0200},
  OPTbiburl    = {https://dblp.org/rec/bib/conf/icml/ThomasB16},
  OPTbibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{chouldechova2017fair,
  title={Fair prediction with disparate impact: A study of bias in recidivism prediction instruments},
  author={Chouldechova, Alexandra},
  journal={Big data},
  volume={5},
  number={2},
  pages={153--163},
  year={2017},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}

@article{mccandless2017comparison,
  title={A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding},
  author={McCandless, Lawrence C and Gustafson, Paul},
  journal={Statistics in medicine},
  volume={36},
  number={18},
  pages={2887--2901},
  year={2017},
  publisher={Wiley Online Library}
}

@article{kleinberg2018human,
  title={Human decisions and machine predictions},
  author={Kleinberg, Jon and Lakkaraju, Himabindu and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil},
  journal={The quarterly journal of economics},
  volume={133},
  number={1},
  pages={237--293},
  year={2018},
  publisher={Oxford University Press}
}

@inproceedings{corbett2017algorithmic,
  title={Algorithmic decision making and the cost of fairness},
  author={Corbett-Davies, Sam and Pierson, Emma and Feller, Avi and Goel, Sharad and Huq, Aziz},
  booktitle={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={797--806},
  year={2017}
}

@inproceedings{coston2020counterfactual,
  title={Counterfactual risk assessments, evaluation, and fairness},
  author={Coston, Amanda and Mishler, Alan and Kennedy, Edward H and Chouldechova, Alexandra},
  booktitle={Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
  pages={582--593},
  year={2020}
}

@inproceedings{madras2019fairness,
  title={Fairness through causal awareness: Learning causal latent-variable models for biased data},
  author={Madras, David and Creager, Elliot and Pitassi, Toniann and Zemel, Richard},
  booktitle={Proceedings of the Conference on Fairness, Accountability, and Transparency},
  pages={349--358},
  year={2019}
}

@article{murder,
OPTISSN = {09641998, 1467985X},
 OPTURL = {http://www.jstor.org/stable/30136747},
 author = {Richard Berk and Lawrence Sherman and Geoffrey Barnes and Ellen Kurtz and Lindsay Ahlman},
 journal = {Journal of the Royal Statistical Society. Series A (Statistics in Society)},
 number = {1},
 pages = {191--211},
 publisher = {[Wiley, Royal Statistical Society]},
 title = {Forecasting Murder within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning},
 volume = {172},
 year = {2009}
}


@book{little2019statistical,
  title={Statistical analysis with missing data},
  author={Little, Roderick JA and Rubin, Donald B},
  volume={793},
  year={2019},
  publisher={John Wiley \& Sons}
}

@article{bang2005doubly,
  title={Doubly robust estimation in missing data and causal inference models},
  author={Bang, Heejung and Robins, James M},
  journal={Biometrics},
  volume={61},
  number={4},
  pages={962--973},
  year={2005},
  publisher={Wiley Online Library}
}

@inproceedings{DBLP:conf/icml/DudikLL11,
  author    = {Miroslav Dud{\'{\i}}k and
               John Langford and
               Lihong Li},
  editor    = {Lise Getoor and
               Tobias Scheffer},
  title     = {Doubly Robust Policy Evaluation and Learning},
  booktitle = {Proceedings of the 28th International Conference on Machine Learning,
               {ICML} 2011, Bellevue, Washington, USA, June 28 - July 2, 2011},
  pages     = {1097--1104},
  publisher = {Omnipress},
  year      = {2011},
  OPTurl       = {https://icml.cc/2011/papers/554\_icmlpaper.pdf},
  OPTtimestamp = {Wed, 03 Apr 2019 17:43:35 +0200},
  OPTbiburl    = {https://dblp.org/rec/bib/conf/icml/DudikLL11},
  OPTbibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Jung2,
author = {Jung, Jongbin and Shroff, Ravi and Feller, Avi and Goel, Sharad},
title = {Bayesian Sensitivity Analysis for Offline Policy Evaluation},
year = {2020},
isbn = {9781450371100},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
OPTurl = {https://doi.org/10.1145/3375627.3375822},
OPTdoi = {10.1145/3375627.3375822},
booktitle = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
pages = {64?70},
numpages = {7},
OPTkeywords = {pretrial risk assessment, offline policy evaluation, sensitivity to unmeasured confounding},
OPTlocation = {New York, NY, USA},
OPTseries = {AIES}
}



@inproceedings{DBLP:conf/icml/JohanssonSS16,
  author    = {Fredrik D. Johansson and
               Uri Shalit and
               David A. Sontag},
  editor    = {Maria{-}Florina Balcan and
               Kilian Q. Weinberger},
  title     = {Learning Representations for Counterfactual Inference},
  booktitle = {Proceedings of the 33nd International Conference on Machine Learning,
               {ICML} 2016, New York City, NY, USA, June 19-24, 2016},
  series    = {{JMLR} Workshop and Conference Proceedings},
  volume    = {48},
  pages     = {3020--3029},
  publisher = {JMLR.org},
  year      = {2016},
  url       = {http://proceedings.mlr.press/v48/johansson16.html},
  timestamp = {Fri, 15 Nov 2019 17:16:09 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/icml/JohanssonSS16},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jmlr/BottouPCCCPRSS13,
  author    = {L{\'{e}}on Bottou and
               Jonas Peters and
               Joaquin Qui{\~{n}}onero Candela and
               Denis Xavier Charles and
               Max Chickering and
               Elon Portugaly and
               Dipankar Ray and
               Patrice Y. Simard and
               Ed Snelson},
  title     = {Counterfactual reasoning and learning systems: the example of computational
               advertising},
  journal   = {J. Mach. Learn. Res.},
  volume    = {14},
  number    = {1},
  pages     = {3207--3260},
  year      = {2013},
  url       = {http://dl.acm.org/citation.cfm?id=2567766},
  timestamp = {Wed, 10 Jul 2019 15:27:56 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/jmlr/BottouPCCCPRSS13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{DBLP:conf/icml/NabiMS19,
  author    = {Razieh Nabi and
               Daniel Malinsky and
               Ilya Shpitser},
               OPTaddress = { {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 OPTeditor    = {Kamalika Chaudhuri and
               Ruslan Salakhutdinov},
  title     = {Learning Optimal Fair Policies},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning},
  series    = {Proceedings of Machine Learning Research},
  volume    = {97},
  pages     = {4674--4682},
  publisher = {{PMLR}},
  year      = {2019},
  OPTurl       = {http://proceedings.mlr.press/v97/nabi19a.html},
  OPTtimestamp = {Tue, 11 Jun 2019 15:37:38 +0200},
  OPTbiburl    = {https://dblp.org/rec/bib/conf/icml/NabiMS19},
  OPTbibsource = {dblp computer science bibliography, https://dblp.org}
}


@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={Oxford University Press}
}

@article{austin2011introduction,
  title={An introduction to propensity score methods for reducing the effects of confounding in observational studies},
  author={Austin, Peter C},
  journal={Multivariate behavioral research},
  volume={46},
  number={3},
  pages={399--424},
  year={2011},
  publisher={Taylor \& Francis}
}

@book{pearl2000,
  author = 	 {J.~Pearl},
  title = 	 {Causality: Models, Reasoning, and Inference},
  publisher = 	 {Cambridge University Press},
  year = 	 {2000}
}

@article{pearl1995empirical,
  title={Causal diagrams for empirical research},
  author={Pearl, Judea},
  journal={Biometrika},
  volume={82},
  number={4},
  pages={669--688},
  year={1995},
  publisher={Oxford University Press}
}


@article{brennan2009evaluating,
  title={Evaluating the predictive validity of the COMPAS risk and needs assessment system},
  author={Brennan, Tim and Dieterich, William and Ehret, Beate},
  journal={Criminal Justice and Behavior},
  volume={36},
  number={1},
  pages={21--40},
  year={2009},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}


@article{hernan2004structural,
  title={A structural approach to selection bias},
  author={Hern{\'a}n, Miguel A and Hern{\'a}ndez-D{\'\i}az, Sonia and Robins, James M},
  journal={Epidemiology},
  volume={15},
  number={5},
  pages={615--625},
  year={2004},
  publisher={LWW}
}

@inproceedings{DBLP:conf/icml/Kusner0LS19,
  author    = {Matt J. Kusner and
               Chris Russell and
               Joshua R. Loftus and
               Ricardo Silva},
  title     = {Making Decisions that Reduce Discriminatory Impacts},
  booktitle = {{ICML}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {97},
  pages     = {3591--3600},
  publisher = {{PMLR}},
  year      = {2019}
}

@inproceedings{tolan2019why,
	OPTAcmid = {3326705},
	OPTAddress = {New York, NY, USA},
	Author = {Tolan, Song\"{u}l and Miron, Marius and G\'{o}mez, Emilia and Castillo, Carlos},
	Booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law},
	OPTDate-Added = {2019-08-02 06:37:23 +0000},
	OPTDate-Modified = {2019-08-02 06:38:23 +0000},
	OPTDoi = {10.1145/3322640.3326705},
	OPTIsbn = {978-1-4503-6754-7},
	OPTKeywords = {algorithmic bias, algorithmic fairness, criminal recidivism, machine learning, risk assessment},
	OPTLocation = {Montreal, QC, Canada},
	OPTNumpages = {10},
	Pages = {83--92},
	Publisher = {ACM},
	OPTSeries = {ICAIL '19},
	Title = {Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia},
	OPTUrl = {http://doi.acm.org/10.1145/3322640.3326705},
	Year = {2019}
	}

@article{pearl2010introduction,
	Author = {Pearl, Judea},
	Journal = {The international journal of biostatistics},
	Number = {2},
	Publisher = {De Gruyter},
	Title = {An introduction to causal inference},
	Volume = {6},
	Year = {2010}}

@book{bookofwhy,
  title={The book of why: the new science of cause and effect},
  author={Pearl, Judea and Mackenzie, Dana},
  year={2018},
  publisher={Basic Books},
  address = {New York, USA}
}

@inproceedings{lakkaraju2017selective,
	OPTAcmid = {3098066},
	OPTAddress = {New York, NY, USA},
	Author = {Lakkaraju, Himabindu and Kleinberg, Jon and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil},
	Booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	OPTDoi = {10.1145/3097983.3098066},
	OPTIsbn = {978-1-4503-4887-4},
	OPTKeywords = {evaluating machine learning algorithms, selective labels, unmeasured confounders, unobservables},
	OPTLocation = {Halifax, NS, Canada},
	Numpages = {10},
	Pages = {275--284},
	Publisher = {ACM},
	Series = {KDD '17},
	Title = {The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables},
	OPTUrl = {http://doi.acm.org/10.1145/3097983.3098066},
	Year = {2017},
	OPTBdsk-Url-1 = {http://doi.acm.org/10.1145/3097983.3098066},
	OPTBdsk-Url-2 = {http://dx.doi.org/10.1145/3097983.3098066}}

@article{jung2018algorithmic,
	Author = {Jung, Jongbin and Shroff, Ravi and Feller, Avi and Goel, Sharad},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Journal = {arXiv preprint arXiv:1805.01868},
	Title = {Algorithmic decision making in the presence of unmeasured confounding},
	Year = {2018}}

@article{dearteaga2018learning,
	Author = {De-Arteaga, Maria and Dubrawski, Artur and Chouldechova, Alexandra},
	Date-Added = {2019-08-02 06:37:23 +0000},
	Date-Modified = {2019-08-02 06:37:23 +0000},
	Journal = {arXiv preprint arXiv:1807.00905},
	Title = {Learning under selective labels in the presence of expert consistency},
	Year = {2018}}

@article{ohagan1979outlier,
  title={On outlier rejection phenomena in Bayes inference},
  author={O'Hagan, Anthony},
  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
  volume={41},
  number={3},
  pages={358--367},
  year={1979},
  publisher={Wiley Online Library}
}

@inproceedings{bareinboim2012controlling,
  title={Controlling selection bias in causal inference},
  author={Bareinboim, Elias and Pearl, Judea},
  booktitle={Artificial Intelligence and Statistics},
  pages={100--108},
  year={2012}
}

@misc{kleinberg2016inherent,
    title={Inherent Trade-Offs in the Fair Determination of Risk Scores},
    author={Jon Kleinberg and Sendhil Mullainathan and Manish Raghavan},
    year={2016},
    eprint={1609.05807},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@article{angwin2016machine, 
  title={Machine Bias}, 
  url={https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing}, 
  journal={ProPublica}, 
  year = {2016},
  author={Julia Angwin and Jeff Larson and Surya Mattu and Lauren Kirchner}
} 

@article{mccandless2007bayesian,
  title={Bayesian sensitivity analysis for unmeasured confounding in observational studies},
  author={McCandless, Lawrence C and Gustafson, Paul and Levy, Adrian},
  journal={Statistics in medicine},
  volume={26},
  number={11},
  pages={2331--2347},
  year={2007},
  publisher={Wiley Online Library}
}