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Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
14 October 2019
Christopher Frye
C. Rowat
Ilya Feige
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Papers citing
"Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability"
29 / 29 papers shown
Title
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
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05 May 2025
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum
Lars Henry Berge Olsen
Jon Lachmann
Annabelle Redelmeier
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124
3
0
02 Apr 2025
Accurate Explanation Model for Image Classifiers using Class Association Embedding
Ruitao Xie
Jingbang Chen
Limai Jiang
Rui Xiao
Yi-Lun Pan
Yunpeng Cai
223
4
0
31 Dec 2024
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
Kaveen Hiniduma
Suren Byna
J. L. Bez
Ravi Madduri
83
7
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27 Jun 2024
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
485
10,591
0
17 Feb 2020
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan
Chenhao Tan
Amit Sharma
OOD
CML
105
207
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06 Dec 2019
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing
Lenon Minorics
Patrick Blobaum
FAtt
CML
74
282
0
29 Oct 2019
The many Shapley values for model explanation
Mukund Sundararajan
A. Najmi
TDI
FAtt
62
635
0
22 Aug 2019
A Causal Bayesian Networks Viewpoint on Fairness
Silvia Chiappa
William S. Isaac
FaML
62
63
0
15 Jul 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
K. Aas
Martin Jullum
Anders Løland
FAtt
TDI
65
624
0
25 Mar 2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma
Sebastian Tschiatschek
Konstantina Palla
José Miguel Hernández-Lobato
Sebastian Nowozin
Cheng Zhang
79
129
0
28 Sep 2018
Path-Specific Counterfactual Fairness
Silvia Chiappa
Thomas P. S. Gillam
CML
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80
340
0
22 Feb 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen
Le Song
Martin J. Wainwright
Michael I. Jordan
MLT
FAtt
149
575
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21 Feb 2018
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg
G. Erion
Su-In Lee
FAtt
TDI
66
1,405
0
12 Feb 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
250
4,272
0
22 Jun 2017
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus
Mateo Rojas-Carulla
Giambattista Parascandolo
Moritz Hardt
Dominik Janzing
Bernhard Schölkopf
FaML
CML
115
584
0
08 Jun 2017
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
22,002
0
22 May 2017
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar
Peyton Greenside
A. Kundaje
FAtt
203
3,881
0
10 Apr 2017
Fairness in Criminal Justice Risk Assessments: The State of the Art
R. Berk
Hoda Heidari
S. Jabbari
Michael Kearns
Aaron Roth
56
998
0
27 Mar 2017
Counterfactual Fairness
Matt J. Kusner
Joshua R. Loftus
Chris Russell
Ricardo M. A. Silva
FaML
224
1,584
0
20 Mar 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
302
2,120
0
24 Oct 2016
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
233
4,330
0
07 Oct 2016
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon M. Kleinberg
S. Mullainathan
Manish Raghavan
FaML
121
1,775
0
19 Sep 2016
XGBoost: A Scalable Tree Boosting System
Tianqi Chen
Carlos Guestrin
809
39,062
0
09 Mar 2016
"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
Censoring Representations with an Adversary
Harrison Edwards
Amos Storkey
AAML
FaML
66
506
0
18 Nov 2015
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.0K
150,312
0
22 Dec 2014
Certifying and removing disparate impact
Michael Feldman
Sorelle A. Friedler
John Moeller
C. Scheidegger
Suresh Venkatasubramanian
FaML
204
1,993
0
11 Dec 2014
How to Explain Individual Classification Decisions
D. Baehrens
T. Schroeter
Stefan Harmeling
M. Kawanabe
K. Hansen
K. Müller
FAtt
137
1,104
0
06 Dec 2009
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