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How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes.
Fair inference on outcomes. In Proceedings of the… AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence , Vol. 2018. NIH Public Access, 1931
Razieh Nabi and Ilya Shpitser. 2018 · 1931
Earlier work this paper cites.
Fairness Without Demographics in Repeated Loss Minimization. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Jennifer Dy and Andreas Krause (Eds.), Vol. 80. PMLR, Stockholmsmässan, Stockholm Sweden, 1929–1938
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018 · 1938
Earlier work this paper cites.
Inference and missing data
Donald B Rubin. 1976 · 1976
Earlier work this paper cites.
Statistics and causal inference
Paul W Holland. 1986 · 1986
Earlier work this paper cites.
Causality in the social sciences
Margaret Mooney Marini and Burton Singer. 1988 · 1988
Earlier work this paper cites.
Enhancing the Outcomes of Low-Birth-Weight, Premature Infants
A Multisite. 1990 · 1990
Earlier work this paper cites.
Identifiability and exchangeability for direct and indirect effects
James M Robins and Sander Greenland. 1992 · 1992
Earlier work this paper cites.
Effects of Early Intervention on Cognitive Function of Low Birth Weight Preterm Infants,
J. Brooks-Gunn, F. Liaw, and P. Klebanov. 1994 · 1994
Earlier work this paper cites.
Designing social inquiry: Scientific inference in qualitative research
Gary King, Robert O Keohane, and Sidney Verba. 1994 · 1994
Earlier work this paper cites.
Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin. 2005 · 2005
Earlier work this paper cites.
An analysis of the New York City police department’s “stop-and-frisk” policy in the context of claims of racial bias
Andrew Gelman, Jeffrey Fagan, and Alex Kiss. 2007 · 2007
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
Earlier work this paper cites.
Causality
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Jonathan AC Sterne, Ian R White, John B Carlin, Michael Spratt, Patrick Royston, Michael G Kenward, Angela M Wood, and James R Carpenter. 2009 · 2009
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill. 2011 · 2011
Earlier work this paper cites.
Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference . ACM, 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 (ICML’14) . JMLR.org, II–1278–II–1286
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Cited alongside, same era.
On causal interpretation of race in regressions adjusting for confounding and mediating variables
Tyler J VanderWeele and Whitney R Robinson. 2014 · 2014
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015 · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization. In International Conference on Learning Representations
Diederik Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
Causal effect inference with deep latent-variable models. In Advances in Neural Information Processing Systems . 6446–6456
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling. 2017 · 2017
Later among the works it cites.
Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, International Convention Centre, Sydney, Australia, 3076–3085
Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017 · 2017
Later among the works it cites.
Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics . 962–970
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi. 2017 · 2017
Later among the works it cites.
A Reductions Approach to Fair Classification. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Jennifer Dy and Andreas Krause (Eds.), Vol. 80. PMLR, Stockholmsmässan, Stockholm Sweden, 60–69
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Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2016 · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning. In Advances in neural information processing systems . 3315–3323
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Learning representations for counterfactual inference. In International Conference on Machine Learning . 3020–3029
Fredrik Johansson, Uri Shalit, and David Sontag. 2016 · 2016
Cited alongside, same era.
The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel. 2016 · 2016
Cited alongside, same era.
To predict and serve?
Kristian Lum and William Isaac. 2016 · 2016
Cited alongside, same era.
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Maya Sen and Omar Wasow. 2016 · 2016
Cited alongside, same era.
Model criticism for bayesian causal inference
Dustin Tran, Francisco JR Ruiz, Susan Athey, and David M Blei. 2016 · 2016
Cited alongside, same era.
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Alexander Alemi, Ben Poole, Ian Fischer, Joshua Dillon, Rif A Saurous, and Kevin Murphy. 2018 · 2018
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Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito, and Jonathan Zittrain. 2018 · 2018
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