Fetching the paper…
Reading the bibliography…
Fair machine learning aims to prevent discrimination against individuals or sub-populations based on sensitive attributes such as gender and race.
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
Earlier work this paper cites.
Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl · 1988
Earlier work this paper cites.
An algorithm for fast recovery of sparse causal graphs
Peter Spirtes and Clark Glymour · 1991
Earlier work this paper cites.
Mixture density networks
Christopher M Bishop · 1994
Earlier work this paper cites.
Testing identifiability of causal effects
David Galles and Judea Pearl · 1995
Earlier work this paper cites.
Causal inference and causal explanation with background knowledge
Christopher Meek · 1995
Earlier work this paper cites.
Causal diagrams for empirical research
Judea Pearl · 1995
Earlier work this paper cites.
Probabilistic evaluation of sequential plans from causal models with hidden variables
Judea Pearl and James M Robins · 1995
Earlier work this paper cites.
A characterization of markov equivalence classes for acyclic digraphs
Steen A Andersson, David Madigan, and Michael D Perlman · 1997
Earlier work this paper cites.
The tetrad project: Constraint based aids to causal model specification
Richard Scheines, Peter Spirtes, Clark Glymour, Christopher Meek, and Thomas Richardson · 1998
Earlier work this paper cites.
Models, reasoning and inference
Judea Pearl et al · 2000
Earlier work this paper cites.
Identifiability of path-specific effects
Chen Avin, Ilya Shpitser, and Judea Pearl · 2005
Earlier work this paper cites.
A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex J Smola · 2007
Earlier work this paper cites.
Using data mining to predict secondary school student performance
Paulo Cortez and Alice Maria Gonçalves Silva · 2008
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, Bernhard Schölkopf, et al · 2008
Earlier work this paper cites.
Evaluating the predictive validity of the compas risk and needs assessment system
Tim Brennan, William Dieterich, and Beate Ehret · 2009
Earlier work this paper cites.
Estimating high-dimensional intervention effects from observational data
Marloes H Maathuis, Markus Kalisch, and Peter Bühlmann · 2009
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
K Zhang and A Hyvärinen · 2009
Earlier work this paper cites.
Consumer credit-risk models via machine-learning algorithms
Amir E Khandani, Adlar J Kim, and Andrew W Lo · 2010
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Alain Hauser and Peter Bühlmann · 2012
Earlier work this paper cites.
Causal discovery of linear acyclic models with arbitrary distributions
Patrik O Hoyer, Aapo Hyvarinen, Richard Scheines, Peter L Spirtes, Joseph Ramsey, Gustavo Lacerda, and Shohei Shimizu · 2012
Earlier work this paper cites.
Discrimination in online ad delivery
Latanya Sweeney · 2013
Earlier work this paper cites.
Order-independent constraint-based causal structure learning
Diego Colombo, Marloes H Maathuis, et al · 2014
Cited alongside, same era.
Identifiability of gaussian structural equation models with equal error variances
Jonas Peters and Peter Bühlmann · 2014
Cited alongside, same era.
Causal discovery with continuous additive noise models
Jonas Peters, Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2014
Cited alongside, same era.
A complete generalized adjustment criterion
Emilija Perković, Johannes Textor, Markus Kalisch, and Marloes H Maathuis · 2015
Cited alongside, same era.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
Cited alongside, same era.
Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
Samuel Yeom and Michael Carl Tschantz · 2018
Later among the works it cites.
Causal modeling-based discrimination discovery and removal: criteria, bounds, and algorithms
Lu Zhang, Yongkai Wu, and Xintao Wu · 2018
Later among the works it cites.
Path-specific counterfactual fairness
Silvia Chiappa · 2019
Later among the works it cites.
Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
Later among the works it cites.
Size of interventional markov equivalence classes in random dag models
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, and Caroline Uhler · 2019
Later among the works it cites.
Fairness in algorithmic decision making: An excursion through the lens of causality
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
Structure learning of linear gaussian structural equation models with weak edges
Marco F Eigenmann, Preetam Nandy, and Marloes H Maathuis · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
Preetam Nandy, Marloes H Maathuis, and Thomas S Richardson · 2017
Cited alongside, same era.
Aria Khademi, Sanghack Lee, David Foley, and Vasant Honavar · 2019
Later among the works it cites.
An overview of ethical issues in using ai systems in hiring with a case study of amazon’s ai based hiring tool
Akhil Alfons Kodiyan · 2019
Later among the works it cites.
Making decisions that reduce discriminatory impacts
Matt Kusner, Chris Russell, Joshua Loftus, and Ricardo Silva · 2019
Later among the works it cites.
Fairness with minimal harm: A pareto-optimal approach for healthcare
N Martinez and M Bertran · 2019
Later among the works it cites.
Interventional fairness: Causal database repair for algorithmic fairness
Babak Salimi, Luke Rodriguez, Bill Howe, and Dan Suciu · 2019
Later among the works it cites.
Unlocking fairness: a trade-off revisited
Michael Wick, Jean-Baptiste Tristan, et al · 2019
Later among the works it cites.
Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon · 2019
Later among the works it cites.
Is there a trade-off between fairness and accuracy? a perspective using mismatched hypothesis testing
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush Varshney · 2020
Later among the works it cites.
Ida with background knowledge
Zhuangyan Fang and Yangbo He · 2020
Later among the works it cites.
Identification and estimation of direct causal effects
Emily R Flanagan · 2020
Later among the works it cites.
Collapsible ida: Collapsing parental sets for locally estimating possible causal effects
Yue Liu, Zhuangyan Fang, Yangbo He, and Zhi Geng · 2020
Later among the works it cites.
Identifying causal effects in maximally oriented partially directed acyclic graphs
Emilija Perkovic · 2020
Later among the works it cites.
Data augmentation for discrimination prevention and bias disambiguation
Shubham Sharma, Yunfeng Zhang, Jesús Aliaga, Djallel Bouneffouf, Vinod Muthusamy, and Ramazon Kush · 2020
Later among the works it cites.
On efficient adjustment in causal graphs
Janine Witte, Leonard Henckel, Marloes H Maathuis, and Vanessa Didelez · 2020
Later among the works it cites.
Learning individually fair classifier with path-specific causal-effect constraint
Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino, and Hisashi Kashima · 2021
Later among the works it cites.
The (im) possibility of fairness: Different value systems require different mechanisms for fair decision making
Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2021
Later among the works it cites.
Minimal enumeration of all possible total effects in a markov equivalence class
Richard Guo and Emilija Perkovic · 2021
Later among the works it cites.
Causal feature selection for algorithmic fairness
Sainyam Galhotra, Karthikeyan Shanmugam, Prasanna Sattigeri, Kush R Varshney, Rachel Bellamy, Kuntal Dey, et al · 2022
Later among the works it cites.
The fairness-accuracy pareto front
Susan Wei and Marc Niethammer · 2022
Later among the works it cites.
Counterfactual fairness with partially known causal graph
Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, and Mingming Gong · 2022
Later among the works it cites.