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Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding.
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 1905
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FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 1909
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FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned, and Maarten de Rijke · 1911
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The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons
Solon Barocas, Andrew D. Selbst, and Manish Raghavan · 1912
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Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan, Chenhao Tan, and Amit Sharma · 1912
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Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal and Ramakrishnan Srikant · 1994
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Non-Monotone Submodular Maximization under Matroid and Knapsack Constraints
Jon Lee, Vahab Mirrokni, Viswanath Nagarjan, and Maxim Sviridenko · 2009
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A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
Earlier work this paper cites.
Counterfactual Explanations for Machine Learning: A Review
Sahil Verma, John Dickerson, and Keegan Hines · 2010
Earlier work this paper cites.
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 · 2018
Cited alongside, same era.
Faithful and Customizable Explanations of Black Box Models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2019
Cited alongside, same era.
Actionable Recourse in Linear Classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Cited alongside, same era.
DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi, and Hiroki Arimura · 2020
Cited alongside, same era.
Explaining Groups of Points in Low-Dimensional Representations
Optimal Counterfactual Explanations in Tree Ensembles
Axel Parmentier and Thibaut Vidal · 2021
Later among the works it cites.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel, Tobias Richter, and Gjergji Kasneci · 2021
Later among the works it cites.
Counterfactual Explanations Can Be Manipulated
Dylan Slack, Anna Hilgard, Himabindu Lakkaraju, and Sameer Singh · 2021
Later among the works it cites.
A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M. Alonso, Alejandro Catala, and Martín Pereira-Fariña · 2021
Later among the works it cites.
Interpretable Counterfactual Explanations Guided by Prototypes
Arnaud Van Looveren and Janis Klaise · 2021
Later among the works it cites.
UCI Machine Learning Repository, 2019
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Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, and Ameet Talwalkar · 2020
Cited alongside, same era.
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
Kaivalya Rawal and Himabindu Lakkaraju · 2020
Cited alongside, same era.
The Philosophical Basis of Algorithmic Recourse
Suresh Venkatasubramanian and Mark Alfano · 2020
Cited alongside, same era.
A Step Towards Global Counterfactual Explanations: Approximating the Feature Space Through Hierarchical Division and Graph Search
Maximilian Becker, Nadia Burkart, Pascal Birnstill, and Jürgen Beyerer · 2021
Cited alongside, same era.
Dheeru Dua and Casey Graff · 2022
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Explainable Machine Learning Challenge, 2018
FICO · 2022
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Rethinking Explainability as a Dialogue: A Practitioner’s Perspective, 2022
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh · 2022
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Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates
Dan Ley, Umang Bhatt, and Adrian Weller · 2022
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