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Asymmetric Shapley values: incorporating causal knowledge into
  model-agnostic explainability
v1v2v3 (latest)

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability

14 October 2019
Christopher Frye
C. Rowat
Ilya Feige
ArXiv (abs)PDFHTML

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
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
Pouria Fatemi
Ehsan Sharifian
Mohammad Hossein Yassaee
90
0
0
05 May 2025
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum
Lars Henry Berge Olsen
Jon Lachmann
Annabelle Redelmeier
TDIFAtt
124
3
0
02 Apr 2025
Accurate Explanation Model for Image Classifiers using Class Association Embedding
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
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
Kaveen Hiniduma
Suren Byna
J. L. Bez
Ravi Madduri
83
7
0
27 Jun 2024
Decision-Making with Auto-Encoding Variational Bayes
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
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Divyat Mahajan
Chenhao Tan
Amit Sharma
OODCML
105
207
0
06 Dec 2019
Feature relevance quantification in explainable AI: A causal problem
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing
Lenon Minorics
Patrick Blobaum
FAttCML
74
282
0
29 Oct 2019
The many Shapley values for model explanation
The many Shapley values for model explanation
Mukund Sundararajan
A. Najmi
TDIFAtt
62
635
0
22 Aug 2019
A Causal Bayesian Networks Viewpoint on Fairness
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
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
K. Aas
Martin Jullum
Anders Løland
FAttTDI
65
624
0
25 Mar 2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial
  VAE
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
Path-Specific Counterfactual Fairness
Silvia Chiappa
Thomas P. S. Gillam
CMLFaML
80
340
0
22 Feb 2018
Learning to Explain: An Information-Theoretic Perspective on Model
  Interpretation
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen
Le Song
Martin J. Wainwright
Michael I. Jordan
MLTFAtt
149
575
0
21 Feb 2018
Consistent Individualized Feature Attribution for Tree Ensembles
Consistent Individualized Feature Attribution for Tree Ensembles
Scott M. Lundberg
G. Erion
Su-In Lee
FAttTDI
66
1,405
0
12 Feb 2018
Explanation in Artificial Intelligence: Insights from the Social
  Sciences
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
250
4,272
0
22 Jun 2017
Avoiding Discrimination through Causal Reasoning
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus
Mateo Rojas-Carulla
Giambattista Parascandolo
Moritz Hardt
Dominik Janzing
Bernhard Schölkopf
FaMLCML
115
584
0
08 Jun 2017
A Unified Approach to Interpreting Model Predictions
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
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
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
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
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
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
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
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
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
1.2K
17,027
0
16 Feb 2016
Censoring Representations with an Adversary
Censoring Representations with an Adversary
Harrison Edwards
Amos Storkey
AAMLFaML
66
506
0
18 Nov 2015
Adam: A Method for Stochastic Optimization
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
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
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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