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The importance of algorithmic fairness grows with the increasing impact machine learning has on people's lives.
Migration, age, and education
Aba Schwartz · 1976
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
James Robins · 1986
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Ethnicity, crime, and immigration
Michael Tonry · 1997
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Direct and indirect effects
Judea Pearl · 2001
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Econometric methods with applications in business and economics
Christiaan Heij, Christiaan Heij, Paul de Boer, Philip Hans Franses, Teun Kloek, Herman K van Dijk, et al · 2004
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Identifiability of path-specific effects
Chen Avin, Ilya Shpitser, and Judea Pearl · 2005
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Method and system for loan origination and underwriting, October 23 2007
John F Mahoney and James M Mohen · 2007
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Neighbourhood ethnic concentration and discrimination
William Magee, Eric Fong, and Rima Wilkes · 2008
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Causality
Judea Pearl · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Lecture 6d-a separate, adaptive learning rate for each connection
G Hinton, N Srivastava, and K Swersky · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding
Ilya Shpitser · 2013
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On the definition of a confounder
Tyler J VanderWeele and Ilya Shpitser · 2013
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Wrong side of the tracks: Big data and protected categories
Simon DeDeo · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gpdr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 2018
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Path-specific counterfactual fairness
Silvia Chiappa and Thomas PS Gillam · 2018
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Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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The variational gaussian process
Dustin Tran, Rajesh Ranganath, and David M Blei · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Unsupervised learning of 3d structure from images
Danilo Jimenez Rezende, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess · 2016
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A causal framework for discovering and removing direct and indirect discrimination
Lu Zhang, Yongkai Wu, and Xintao Wu · 2016
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Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
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Counterfactual risk assessments, evaluation, and fairness
Amanda Coston, Alexandra Chouldechova, and Edward H Kennedy · 2019
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Comment: Reflections on the deconfounder
Alexander D’Amour · 2019
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Fairness through causal awareness: Learning causal latent-variable models for biased data
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2019
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 2019
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Optimal training of fair predictive models
Razieh Nabi, Daniel Malinsky, and Ilya Shpitser · 2019
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Comment on “blessings of multiple causes”
Elizabeth L Ogburn, Ilya Shpitser, and Eric J Tchetgen Tchetgen · 2019
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The blessings of multiple causes
Yixin Wang and David M Blei · 2019
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