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Antti Hyttinen authoredAntti Hyttinen authored
biblio.bib 11.36 KiB
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@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
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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},
editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
title = {Learning Optimal Fair Policies},
booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
series = {Proceedings of Machine Learning Research},
volume = {97},
pages = {4674--4682},
publisher = {{PMLR}},
year = {2019},
url = {http://proceedings.mlr.press/v97/nabi19a.html},
timestamp = {Tue, 11 Jun 2019 15:37:38 +0200},
biburl = {https://dblp.org/rec/bib/conf/icml/NabiMS19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{rosenbaum1983central,
title={The central role of the propensity score in observational studies for causal effects},
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year={1983},
publisher={Oxford University Press}
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@article{austin2011introduction,
title={An introduction to propensity score methods for reducing the effects of confounding in observational studies},
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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,
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@article{brennan2009evaluating,
title={Evaluating the predictive validity of the COMPAS risk and needs assessment system},
author={Brennan, Tim and Dieterich, William and Ehret, Beate},
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volume={36},
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pages={21--40},
year={2009},
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}
@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},
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@inproceedings{DBLP:conf/icml/Kusner0LS19,
author = {Matt J. Kusner and
Chris Russell and
Joshua R. Loftus and
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title = {Making Decisions that Reduce Discriminatory Impacts},
booktitle = {{ICML}},
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publisher = {{PMLR}},
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@inproceedings{tolan2019why,
Acmid = {3326705},
Address = {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},
Date-Added = {2019-08-02 06:37:23 +0000},
Date-Modified = {2019-08-02 06:38:23 +0000},
Doi = {10.1145/3322640.3326705},
Isbn = {978-1-4503-6754-7},
Keywords = {algorithmic bias, algorithmic fairness, criminal recidivism, machine learning, risk assessment},
Location = {Montreal, QC, Canada},
Numpages = {10},
Pages = {83--92},
Publisher = {ACM},
Series = {ICAIL '19},
Title = {Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia},
Url = {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},
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year={1979},
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@inproceedings{bareinboim2012controlling,
title={Controlling selection bias in causal inference},
author={Bareinboim, Elias and Pearl, Judea},
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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}
}