Explaining and visualizing black-box models through counterfactual paths

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Explaining and visualizing black-box models through counterfactual paths

15 July 2023

Bastian Pfeifer

Mateusz Krzyzinski

Hubert Baniecki

Andreas Holzinger

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Papers citing "Explaining and visualizing black-box models through counterfactual paths"

11 / 11 papers shown

Title
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse Martin Pawelczyk Teresa Datta Johannes van-den-Heuvel Gjergji Kasneci Himabindu Lakkaraju 60 38 0 13 Mar 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 54 23 0 22 Feb 2022
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process Christoph Molnar Timo Freiesleben Gunnar Konig Giuseppe Casalicchio Marvin N. Wright B. Bischl 27 61 0 03 Sep 2021
Grouped Feature Importance and Combined Features Effect Plot Quay Au J. Herbinger Clemens Stachl B. Bischl Giuseppe Casalicchio FAtt 81 46 0 23 Apr 2021
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach Christoph Molnar Gunnar Konig B. Bischl Giuseppe Casalicchio 67 81 0 08 Jun 2020
Multi-Objective Counterfactual Explanations Susanne Dandl Christoph Molnar Martin Binder B. Bischl 64 259 0 23 Apr 2020
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 91 322 0 27 May 2019
Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio Christoph Molnar B. Bischl FAtt 38 183 0 18 Apr 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 117 2,360 0 01 Nov 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package M. Scutari CML 108 166 0 30 Jun 2014

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