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Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods).
Generalized linear models
Peter McCullagh and John A. Nelder · 1989
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Error bounds in mathematical programming
Jong-Shi Pang · 1997
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Leo Breiman · 2001
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2002
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Generalized inverse classification
Michael T Lash, Qihang Lin, Nick Street, Jennifer G Robinson, and Jeffrey Ohlmann · 2017
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Inverse classification for comparison-based interpretability in machine learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Interpretable predictions of tree-based ensembles via actionable feature tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
Auditing black-box models for indirect influence
Philip Adler, Casey Falk, Sorelle A Friedler, Tionney Nix, Gabriel Rybeck, Carlos Scheidegger, Brandon Smith, and Suresh Venkatasubramanian · 2018
Cited alongside, same era.
Analysis of classifiers’ robustness to adversarial perturbations
Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2018
Cited alongside, same era.
Interpretable credit application predictions with counterfactual explanations
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
Later among the works it cites.
Preserving causal constraints in counterfactual explanations for machine learning classifiers, 2019
Divyat Mahajan, Chenhao Tan, and Amit Sharma · 2019
Later among the works it cites.
Predictive multiplicity in classification
Charles T Marx, Flavio du Pin Calmon, and Berk Ustun · 2019
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Efficient search for diverse coherent explanations
Christopher Russell · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Later among the works it cites.
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Rory Mc Grath, Luca Costabello, Chan Le Van, Paul Sweeney, Farbod Kamiab, Zhao Shen, and Freddy Lecue · 2018
Cited alongside, same era.
Handling incomplete heterogeneous data using vaes
Alfredo Nazabal, Pablo M Olmos, Zoubin Ghahramani, and Isabel Valera · 2018
Cited alongside, same era.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
Cited alongside, same era.
Model-agnostic counterfactual explanations for consequential decisions
Amir-Hossein Karimi, Gilles Barthe, Borja Belle, and Isabel Valera
Cited in the paper.
Issues with post-hoc counterfactual explanations: a discussion
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, and Marcin Detyniecki
Cited in the paper.
The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki
Cited in the paper.
The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
Closest in time.
Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K. Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Learning counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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