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@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}
}
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@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,
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}
}
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Acmid = {3098066},
Address = {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},
Doi = {10.1145/3097983.3098066},
Isbn = {978-1-4503-4887-4},
Keywords = {evaluating machine learning algorithms, selective labels, unmeasured confounders, unobservables},
Location = {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},
Url = {http://doi.acm.org/10.1145/3097983.3098066},
Year = {2017},
Bdsk-Url-1 = {http://doi.acm.org/10.1145/3097983.3098066},
Bdsk-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}}
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committed
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@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},
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}
}