%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Laine, Riku P at 2019-08-02 09:38:23 +0300 %% Saved with string encoding Unicode (UTF-8) @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}, 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}, 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, 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}, 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} }