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Preserving Causal Constraints in Counterfactual Explanations for Machine
  Learning Classifiers

Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

6 December 2019
Divyat Mahajan
Chenhao Tan
Amit Sharma
    OOD
    CML
ArXivPDFHTML

Papers citing "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"

12 / 62 papers shown
Title
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
Ricards Marcinkevics
Julia E. Vogt
XAI
28
119
0
03 Dec 2020
Towards Unifying Feature Attribution and Counterfactual Explanations:
  Different Means to the Same End
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
R. Mothilal
Divyat Mahajan
Chenhao Tan
Amit Sharma
FAtt
CML
27
99
0
10 Nov 2020
Counterfactual Explanations and Algorithmic Recourses for Machine
  Learning: A Review
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma
Varich Boonsanong
Minh Hoang
Keegan E. Hines
John P. Dickerson
Chirag Shah
CML
26
164
0
20 Oct 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in
  Counterfactual Explanations
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations
Andrea Ferrario
M. Loi
25
5
0
09 Oct 2020
The Intriguing Relation Between Counterfactual Explanations and
  Adversarial Examples
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
Timo Freiesleben
GAN
43
62
0
11 Sep 2020
On Generating Plausible Counterfactual and Semi-Factual Explanations for
  Deep Learning
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Eoin M. Kenny
Mark T. Keane
28
99
0
10 Sep 2020
On Counterfactual Explanations under Predictive Multiplicity
On Counterfactual Explanations under Predictive Multiplicity
Martin Pawelczyk
Klaus Broelemann
Gjergji Kasneci
25
85
0
23 Jun 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic
  approach
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi
Julius von Kügelgen
Bernhard Schölkopf
Isabel Valera
CML
28
178
0
11 Jun 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Amir-Hossein Karimi
Bernhard Schölkopf
Isabel Valera
CML
24
337
0
14 Feb 2020
Two Causal Principles for Improving Visual Dialog
Two Causal Principles for Improving Visual Dialog
Jiaxin Qi
Yulei Niu
Jianqiang Huang
Hanwang Zhang
CML
16
146
0
24 Nov 2019
Asymmetric Shapley values: incorporating causal knowledge into
  model-agnostic explainability
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye
C. Rowat
Ilya Feige
16
180
0
14 Oct 2019
Explaining Visual Models by Causal Attribution
Explaining Visual Models by Causal Attribution
Álvaro Parafita
Jordi Vitrià
CML
FAtt
62
35
0
19 Sep 2019
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