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Recently there has been sustained interest in modifying prediction algorithms to satisfy fairness constraints.
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Eric J. Tchetgen Tchetgen and Ilya Shpitser · 2012
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Quantifying explainable discrimination and removing illegal discrimination in automated decision making
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Ilya Shpitser · 2013
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All of Statistics: A Concise Course in Statistical Inference
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Eric J. Tchetgen Tchetgen and Ilya Shpitser · 2014
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Causal inference with a graphical hierarchy of interventions
Ilya Shpitser and Eric J. Tchetgen Tchetgen · 2016
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Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Fairness in decision-making – the causal explanation formula
Junzhe Zhang and Elias Bareinboim · 2018
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Identification in missing data models represented by directed acyclic graphs
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, and James Robins · 2019
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Path-specific counterfactual fairness
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Razieh Nabi, Daniel Malinsky, and Ilya Shpitser · 2019
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Rohit Bhattacharya, Razieh Nabi, and Ilya Shpitser · 2020
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Evaluating the impact of prediction models: lessons learned, challenges, and recommendations
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Shira Mitchell, Eric Potash, and Solon Barocas · 2018
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Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva
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