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Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
M E Charlson, P Pompei, K L Ales, and C R MacKenzie · 1987
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl · 1988
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Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases
R A Deyo, D C Cherkin, and M A Ciol · 1992
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Comorbidity measures for use with administrative data
A Elixhauser, C Steiner, D R Harris, and R M Coffey · 1998
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Physician risk assessment and APACHE scores in cardiac care units
G L Pierpont and C M Parenti · 1999
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PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals
A L Goldberger, L A Amaral, L Glass, J M Hausdorff, P C Ivanov, R G Mark, J E Mietus, G B Moody, C K Peng, and H E Stanley · 2000
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Causality: Models, Reasoning, and Inference
Judea Pearl · 2000
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Measuring potentially avoidable hospital readmissions
Patricia Halfon, Yves Eggli, Guy van Melle, Julia Chevalier, Jean Blaise Wasserfallen, and Bernard Burnand · 2002
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Novelty detection: a review—part 1: statistical approaches
Markos Markou and Sameer Singh · 2003
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Causal inference with general treatment regimes
Kosuke Imai and David A van Dyk · 2004
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Probabilistic Networks and Expert Systems, Exact Computational Methods for Bayesian Networks
Robert G. Cowell, A. Philip Dawid, Steffen Lauritzen, and David J. Spiegelhalter · 2007
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Risk adjustment performance of charlson and elixhauser comorbidities in ICD-9 and ICD-10 administrative databases
Bing Li, Dewey Evans, Peter Faris, Stafford Dean, and Hude Quan · 2008
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman · 2009
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A modification of the elixhauser comorbidity measures into a point system for hospital death using administrative data
Carl van Walraven, Peter C Austin, Alison Jennings, Hude Quan, and Alan J Forster · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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A kernel Two-Sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, and Bernhard Scholkopf · 2012
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij · 2012
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A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions
Jenna Wiens, John Guttag, and Eric Horvitz · 2014
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MIMIC-III, a freely accessible critical care database
Alistair E W Johnson, Tom J Pollard, Lu Shen, Li-Wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and Dingding Wang · 2016
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Discriminatory transfer
Chao Lan and Jun Huan · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, and Hanna Wallach · 2018
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The MIMIC code repository: enabling reproducibility in critical care research
Alistair Ew Johnson, David J Stone, Leo A Celi, and Tom J Pollard · 2018
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Residual unfairness in fair machine learning from prejudiced data
Nathan Kallus and Angela Zhou · 2018
Cited alongside, same era.
Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Domain adaptation by using causal inference to predict invariant conditional distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2018
Cited alongside, same era.
Scalable and accurate deep learning with electronic health records
A deep learning system for differential diagnosis of skin diseases
Yuan Liu, Ayush Jain, Clara Eng, David H Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, Vishakha Gupta, Nalini Singh, Vivek Natarajan, Rainer Hofmann-Wellenhof, Greg S Corrado, Lily H Peng, Dale R Webster, Dennis Ai, Susan J Huang, Yun Liu, R Carter Dunn, and David Coz · 2020
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Population-aware hierarchical bayesian domain adaptation via multi-component invariant learning
Vishwali Mhasawade, Nabeel Abdur Rehman, and Rumi Chunara · 2020
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Joint causal inference from multiple contexts
Joris M Mooij, Sara Magliacane, and Tom Claassen · 2020
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Fairness in machine learning
Luca Oneto and Silvia Chiappa · 2020
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Fairness warnings and fair-MAML: learning fairly with minimal data
Dylan Slack, Sorelle A Friedler, and Emile Givental · 2020
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Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H Shah, Atul J Butte, Michael D Howell, Claire Cui, Greg S Corrado, and Jeffrey Dean · 2018
Cited alongside, same era.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
Cited alongside, same era.
Fairness and machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Cited alongside, same era.
Path-Specific counterfactual fairness
Silvia Chiappa · 2019
Cited alongside, same era.
Fair transfer learning with missing protected attributes
Amanda Coston, Karthikeyan Natesan Ramamurthy, Dennis Wei, Kush R. Varshney, Skyler Speakman, Zairah Mustahsan, and Supriyo Chakraborty · 2019
Cited alongside, same era.
Addressing extreme propensity scores via the overlap weights
Fan Li, Laine E Thomas, and Fan Li · 2019
Cited alongside, same era.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
Cited alongside, same era.
Adarsh Subbaswamy, Roy Adams, and Suchi Saria · 2020
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A distributionally robust approach to fair classification, 2020
Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, and José H. Blanchet · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 2020
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Individual fairness revisited: Transferring techniques from adversarial robustness
Samuel Yeom and Matt Fredrikson · 2020
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Fair meta-learning for few-shot classification
Chen Zhao, Changbin Li, Jincheng Li, and Feng Chen · 2020
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A near-optimal algorithm for debiasing trained machine learning models
Ibrahim Alabdulmohsin and Mario Lučić · 2021
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Permutation weighting
David Arbour, Drew Dimmery, and Arjun Sondhi · 2021
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Ethical machine learning in healthcare
Irene Y Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi · 2021
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Technical challenges for training fair neural networks
Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P Dickerson, and Tom Goldstein · 2021
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Environment inference for invariant learning
Elliot Creager, Joern-Henrik Jacobsen, and Richard Zemel · 2021
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Fair and robust classification under sample selection bias
Wei Du and Xintao Wu · 2021
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Partial identifiability in discrete data with measurement error
Noam Finkelstein, Roy Adams, Suchi Saria, and Ilya Shpitser · 2021
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The clinician and dataset shift in artificial intelligence
Samuel G Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, Isaac S Kohane, and Suchi Saria · 2021
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Fairness through robustness: Investigating robustness disparity in deep learning
Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, and John P Dickerson · 2021
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An empirical characterization of fair machine learning for clinical risk prediction
Stephen R Pfohl, Agata Foryciarz, and Nigam H Shah · 2021
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Re-imagining algorithmic fairness in india and beyond
Nithya Sambasivan, Erin Arnesen, Ben Hutchinson, Tulsee Doshi, and Vinodkumar Prabhakaran · 2021
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Fairness violations and mitigation under covariate shift
Harvineet Singh, Rina Singh, Vishwali Mhasawade, and Rumi Chunara · 2021
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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Nenad Tomašev, Natalie Harris, Sebastien Baur, Anne Mottram, Xavier Glorot, Jack W Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Valerio Magliulo, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, Alistair Connell, Cían O Hughes, Alan Karthikesalingam, Julien Cornebise, Hugh Montgomery, Geraint Rees, Chris Laing, Clifton R Baker, Thomas F Osborne, Ruth Reeves, Demis Hassabis, Dominic King, Mustafa Suleyman, Trevor Back, Christopher Nielson, Martin G Seneviratne, Joseph R Ledsam, and Shakir Mohamed · 2021
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Wenjun Zeng, and Tao Qin · 2021
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Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial
F Perry Wilson, Melissa Martin, Yu Yamamoto, Caitlin Partridge, Erica Moreira, Tanima Arora, Aditya Biswas, Harold Feldman, Amit X Garg, Jason H Greenberg, Monique Hinchcliff, Stephen Latham, Fan Li, Haiqun Lin, Sherry G Mansour, Dennis G Moledina, Paul M Palevsky, Chirag R Parikh, Michael Simonov, Jeffrey Testani, and Ugochukwu Ugwuowo · 2021
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang · 2021
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An empirical framework for domain generalization in clinical settings
Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, and Marzyeh Ghassemi · 2021
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Generalizability challenges of mortality risk prediction models: A retrospective analysis on a multi-center database
Harvineet Singh, Vishwali Mhasawade, and Rumi Chunara · 2022
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