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Algorithmic fairness is typically studied from the perspective of predictions.
Sharma, S.; Henderson, J.; and Ghosh, J. 2019 · 1905
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Equalizing Recourse across Groups
Gupta, V.; Nokhiz, P.; Roy, C. D.; and Venkatasubramanian, S. 2019 · 1909
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Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
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Learning with Kernels
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Structural equations, treatment effects, and econometric policy evaluation 1
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Causality
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Building bridges between structural and program evaluation approaches to evaluating policy
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
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Scikit-learn: Machine learning in Python
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Fairness through awareness
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A kernel two-sample test
Gretton, A.; Borgwardt, K. M.; Rasch, M. J.; Schölkopf, B.; and Smola, A. 2012 · 2012
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UCI machine learning repository
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Learning fair representations
Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013 · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J.; Mohamed, S.; and Wierstra, D. 2014 · 2014
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Equality of Opportunity
Arneson, R. 2015 · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K.; Lee, H.; and Yan, X. 2015 · 2015
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Causal reasoning for algorithmic fairness
Loftus, J. R.; Russell, C.; Kusner, M. J.; and Silva, R. 2018 · 2018
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Fair inference on outcomes
Nabi, R.; and Shpitser, I. 2018 · 2018
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Path-specific counterfactual fairness
Chiappa, S. 2019 · 2019
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Learning optimal fair policies
Nabi, R.; Malinsky, D.; and Shpitser, I. 2019 · 2019
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Interventional fairness: Causal database repair for algorithmic fairness
Salimi, B.; Rodriguez, L.; Howe, B.; and Suciu, D. 2019 · 2019
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Actionable recourse in linear classification
Ustun, B.; Spangher, A.; and Liu, Y. 2019 · 2019
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. 2017 · 2017
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On Fairness, Diversity, and Randomness in Algorithmic Decision Making
Grgić-Hlača, N.; Zafar, M. B.; Gummadi, K.; and Weller, A. 2017 · 2017
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Avoiding discrimination through causal reasoning
Kilbertus, N.; Carulla, M. R.; Parascandolo, G.; Hardt, M.; Janzing, D.; and Schölkopf, B. 2017 · 2017
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Counterfactual fairness
Kusner, M. J.; Loftus, J.; Russell, C.; and Silva, R. 2017 · 2017
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Elements of causal inference
Peters, J.; Janzing, D.; and Schölkopf, B. 2017 · 2017
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When worlds collide: integrating different counterfactual assumptions in fairness
Russell, C.; Kusner, M. J.; Loftus, J.; and Silva, R. 2017 · 2017
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Counterfactual fairness: Unidentification, bound and algorithm
Wu, Y.; Zhang, L.; and Wu, X. 2019 · 2019
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Pc-fairness: A unified framework for measuring causality-based fairness
Wu, Y.; Zhang, L.; Wu, X.; and Tong, H. 2019 · 2019
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The hidden assumptions behind counterfactual explanations and principal reasons
Barocas, S.; Selbst, A. D.; and Raghavan, M. 2020 · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K.; Sharma, A.; and Tan, C. 2020 · 2020
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The philosophical basis of algorithmic recourse
Venkatasubramanian, S.; and Alfano, M. 2020 · 2020
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Algorithmic recourse: from counterfactual explanations to interventions
Karimi, A.-H.; Schölkopf, B.; and Valera, I. 2021 · 2021
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Towards Robust and Reliable Algorithmic Recourse
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