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Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration.
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Jin Tian and Judea Pearl · 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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A general identification condition for causal effects
Jin Tian and Judea Pearl · 2002
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Ilya Shpitser and Judea Pearl · 2006
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Christopher KI Williams and Carl Edward Rasmussen · 2006
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The tradeoffs of large scale learning
Léon Bottou and Olivier Bousquet · 2008
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Complete identification methods for the causal hierarchy
Ilya Shpitser and Judea Pearl · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael I Jordan · 2008
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Nonlinear causal discovery with additive noise models
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, and Bernhard Schölkopf · 2009
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Causality
Judea Pearl · 2009
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On the identifiability of the post-nonlinear causal model
K Zhang and A Hyvärinen · 2009
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Causal inference using the algorithmic markov condition
Dominik Janzing and Bernhard Scholkopf · 2010
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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When worlds collide: integrating different counterfactual assumptions in fairness
Chris Russell, Matt J Kusner, Joshua Loftus, and Ricardo Silva · 2017
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Reliable decision support using counterfactual models
Peter Schulam and Suchi Saria · 2017
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Conditional density estimation with bayesian normalising flows
Brian L Trippe and Richard E Turner · 2018
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Path-specific counterfactual fairness
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Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
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Gaussian process structural equation models with latent variables
Ricardo Silva and Robert B Gramacy · 2010
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Preface to the special issue on data mining for personalised educational systems
Cristóbal Romero and Sebastián Ventura · 2011
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Lecture notes: Gaussian identities
Marc Toussaint · 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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Predictive policing: The role of crime forecasting in law enforcement operations
Walt L Perry · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Silvia Chiappa · 2019
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Equalizing recourse across groups
Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, and Suresh Venkatasubramanian · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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Fair decisions despite imperfect predictions
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf, Krikamol Muandet, and Isabel Valera · 2019
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Preserving causal constraints in counterfactual explanations for machine learning classifiers
Divyat Mahajan, Chenhao Tan, and Amit Sharma · 2019
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FACE: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf, and Adrian Weller · 2019
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The blessings of multiple causes
Yixin Wang and David M Blei · 2019
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The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind K Mothilal, Amit Sharma, and Chenhao Tan · 2020
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Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models
Shubham Sharma, Jette Henderson, and Joydeep Ghosh · 2020
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The philosophical basis of algorithmic recourse
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On the fairness of causal algorithmic recourse
Julius von Kügelgen, Umang Bhatt, Amir-Hossein Karimi, Isabel Valera, Adrian Weller, and Bernhard Schölkopf · 2020
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