Fetching the paper…
Reading the bibliography…
Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc.
The interpretation of interaction in contingency tables
Edward H Simpson · 1951
Earlier work this paper cites.
Another look at “cultural fairness”
Richard B Darlington · 1971
Earlier work this paper cites.
Sex bias in graduate admissions: Data from berkeley
Peter J Bickel, Eugene A Hammel, and J William O’Connell · 1975
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
Earlier work this paper cites.
Statistical methods for research workers
Ronald Aylmer Fisher · 1992
Earlier work this paper cites.
Testing identifiability of causal effects
David Galles and Judea Pearl · 1995
Earlier work this paper cites.
Statistics, 3rd edn., pp. a-107, 1998
D Freedman, R Pisani, and R Purves · 1998
Earlier work this paper cites.
Lsac national longitudinal bar passage study. lsac research report series
Linda F Wightman · 1998
Earlier work this paper cites.
Identifiability in causal bayesian networks: A sound and complete algorithm
Yimin Huang and Marco Valtorta · 1999
Earlier work this paper cites.
Direct and indirect effects
Judea Pearl · 2001
Earlier work this paper cites.
A data-driven software tool for enabling cooperative information sharing among police departments
Michael Redmond and Alok Baveja · 2002
Earlier work this paper cites.
A general identification condition for causal effects
Jin Tian and Judea Pearl · 2002
Earlier work this paper cites.
On the identification of causal effects
Jin Tian and Ilya Shpitser · 2003
Earlier work this paper cites.
Identifying linear causal effects
Jin Tian · 2004
Earlier work this paper cites.
Identifiability of path-specific effects
Chen Avin, Ilya Shpitser, and Judea Pearl · 2005
Earlier work this paper cites.
Identification of conditional interventional distributions
Ilya Shpitser and Judea Pearl · 2006
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
Earlier work this paper cites.
Situation testing for employment discrimination in the united states of america
Marc Bendick · 2007
Earlier work this paper cites.
What counterfactuals can be tested
Ilya Shpitser and Judea Pearl · 2007
Earlier work this paper cites.
Complete identification methods for the causal hierarchy
Ilya Shpitser and Judea Pearl · 2008
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Cited alongside, same era.
Matching methods for causal inference: A review and a look forward
Elizabeth A Stuart · 2010
Cited alongside, same era.
Doubly robust estimation of causal effects
Michele Jonsson Funk, Daniel Westreich, Chris Wiesen, Til Stürmer, M Alan Brookhart, and Marie Davidian · 2011
Cited alongside, same era.
Weight trimming and propensity score weighting
Brian K Lee, Justin Lessler, and Elizabeth A Stuart · 2011
Cited alongside, same era.
A multidisciplinary survey on discrimination analysis, 2011
Andrea Romei and Salvatore Ruggieri · 2011
Cited alongside, same era.
Judea pearl on potential outcomes, 2012
Judea Pearl · 2012
Cited alongside, same era.
Anti-discrimination learning: a causal modeling-based framework
Lu Zhang and Xintao Wu · 2017
Later among the works it cites.
Causal reasoning for algorithmic fairness
Joshua R Loftus, Chris Russell, Matt J Kusner, and Ricardo Silva · 2018
Later among the works it cites.
Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
Later among the works it cites.
The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
Later among the works it cites.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
Later among the works it cites.
Path-specific counterfactual fairness
Silvia Chiappa · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ilya Shpitser · 2013
Cited alongside, same era.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Cited alongside, same era.
Counterfactuals and causal inference
Stephen L Morgan and Christopher Winship · 2015
Cited alongside, same era.
The unfair effects of impact on teachers with the toughest jobs
Kimberly Quick · 2015
Cited alongside, same era.
Machine bias. propublica
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Recursive partitioning for heterogeneous causal effects
Susan Athey and Guido Imbens · 2016
Cited alongside, same era.
Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
Later among the works it cites.
Fairness in algorithmic decision making: An excursion through the lens of causality
Aria Khademi, Sanghack Lee, David Foley, and Vasant Honavar · 2019
Later among the works it cites.
Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
Later among the works it cites.
A potential outcomes calculus for identifying conditional path-specific effects
Daniel Malinsky, Ilya Shpitser, and Thomas Richardson · 2019
Later among the works it cites.
Counterfactual reasoning for fair clinical risk prediction
Stephen R Pfohl, Tony Duan, Daisy Yi Ding, and Nigam H Shah · 2019
Later among the works it cites.
Impact: The dcps evaluation and feedback system for school-based personnel, 2019
Michelle Rhee · 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.
An evaluation toolkit to guide model selection and cohort definition in causal inference
Yishai Shimoni, Ehud Karavani, Sivan Ravid, Peter Bak, Tan Hung Ng, Sharon Hensley Alford, Denise Meade, and Yaara Goldschmidt · 2019
Later among the works it cites.
Mean difference, standardized mean difference (smd), and their use in meta-analysis: As simple as it gets
Chittaranjan Andrade · 2020
Closest in time.
A survey of learning causality with data: Problems and methods
Ruocheng Guo, Lu Cheng, Jundong Li, P Richard Hahn, and Huan Liu · 2020
Closest in time.
Fairness through equality of effort
Wen Huan, Yongkai Wu, Lu Zhang, and Xintao Wu · 2020
Closest in time.
Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang · 2020
Closest in time.
Tracing causal paths from experimental and observational data
Xiang Zhou and Teppei Yamamoto · 2020
Closest in time.