On Measuring Intrinsic Causal Attributions in Deep Neural Networks | Paper Citation Record

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This paper cites Open XAI : Towards a transparent evaluation of model explanations.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Open XAI : Towards a transparent evaluation of model explanations

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This paper cites A causal framework for explaining the predictions of black-box sequence-to-sequence models.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks A causal framework for explaining the predictions of black-box sequence-to-sequence models

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Athreya and Soumen N

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Fairness seen as global sensitivity analysis

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks o baum, Peter G \

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Random forests

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This paper cites Cage: Causality-aware shapley value for global explanations.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Cage: Causality-aware shapley value for global explanations

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Language models are few-shot learners

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This paper cites Neural network attributions: A causal perspective.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Neural network attributions: A causal perspective

Reference 9

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This paper cites Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Beyond First-Order Uncertainty Estimation with Evidential Models for Open-World Recognition

Reference 10

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This paper cites Bach, and Himabindu Lakkaraju.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Bach, and Himabindu Lakkaraju

Reference 11

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Multi-objective counterfactual explanations

Reference 12

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This paper cites Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals

Reference 13

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This paper cites Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis

Reference 14

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This paper cites Distilling a Neural Network Into a Soft Decision Tree.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Distilling a Neural Network Into a Soft Decision Tree

Reference 15

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This paper cites Shapley explainability on the data manifold.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Shapley explainability on the data manifold

Reference 16

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This paper cites Axioms of causal relevance.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Axioms of causal relevance

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks On integration methods based on scrambled nets of arbitrary size

Reference 18

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This paper cites Explaining Classifiers with Causal Concept Effect (CaCE).

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Explaining Classifiers with Causal Concept Effect (CaCE)

Reference 19

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This paper cites Counterfactual visual explanations.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Counterfactual visual explanations

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This paper cites Causal shapley values: Exploiting causal knowledge to explain individual predictions of complex models.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causal shapley values: Exploiting causal knowledge to explain individual predictions of complex models

Reference 21

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This paper cites Generalized functional anova diagnostics for high-dimensional functions of dependent variables.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Generalized functional anova diagnostics for high-dimensional functions of dependent variables

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This paper cites Global explanations of neural networks: Mapping the landscape of predictions.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Global explanations of neural networks: Mapping the landscape of predictions

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This paper cites Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates

Reference 24

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Unresolved cited work

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This paper cites Feature relevance quantification in explainable ai: A causal problem.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Feature relevance quantification in explainable ai: A causal problem

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This paper cites Quantifying intrinsic causal contributions via structure preserving interventions.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Quantifying intrinsic causal contributions via structure preserving interventions

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causal normalizing flows: from theory to practice

Reference 28

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks On measuring causal contributions via do-interventions

Reference 29

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Balasubramanian, and Amit Sharma

Reference 30

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Kingma, and Aapo Hyv \"a rinen

Reference 31

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Knuth and Jayme L

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Towards unifying feature attribution and counterfactual explanations: Different means to the same end

Reference 33

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Kucherenko, S

Reference 34

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Backtracking counterfactuals

Reference 35

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This paper cites Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms, pages 155--169.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Global Sensitivity Analysis for the Interpretation of Machine Learning Algorithms, pages 155--169

Reference 36

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Bach, and Jure Leskovec

Reference 37

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This paper cites How we analyzed the compas recidivism algorithm, 2016.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks How we analyzed the compas recidivism algorithm, 2016

Reference 38

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Randomized quasi-monte carlo: An introduction for practitioners

Reference 39

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This paper cites Recent Advances in Randomized Quasi-Monte Carlo Methods, pages 419--474.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Recent Advances in Randomized Quasi-Monte Carlo Methods, pages 419--474

Reference 40

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Yelvington, Oluwayemisi O

Reference 41

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Lundberg and Su-In Lee

Reference 42

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

Reference 43

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Sampling permutations for shapley value estimation

Reference 44

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Unresolved cited work

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Unresolved cited work

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Owen and Daniel Rudolf

Reference 47

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Masked autoregressive flow for density estimation

Reference 48

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Normalizing flows for probabilistic modeling and inference

Reference 49

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Castro, and Ben Glocker

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causality

Reference 51

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks RISE: randomized input sampling for explanation of black-box models

Reference 52

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Counterfactual data augmentation using locally factored dynamics

Reference 53

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This paper cites Causal fairness analysis: A causal toolkit for fair machine learning.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causal fairness analysis: A causal toolkit for fair machine learning

Reference 54

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This paper cites A comprehensive comparison of total-order estimators for global sensitivity analysis.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks A comprehensive comparison of total-order estimators for global sensitivity analysis

Reference 55

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Unresolved cited work

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks A generalized anova dimensional decomposition for dependent probability measures

Reference 57

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This paper cites Balasubramanian, V Varshaneya, and Satya Narayanan Kar.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Balasubramanian, V Varshaneya, and Satya Narayanan Kar

Reference 58

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This paper cites On Counterfactual Data Augmentation Under Confounding.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks On Counterfactual Data Augmentation Under Confounding

Reference 59

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Retzlaff, Alessa Angerschmid, Anna Saranti, David Schneeberger, Richard Röttger, Heimo Müller, and Andreas Holzinger

Reference 60

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This paper cites why should i trust you?.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks why should i trust you?

Reference 62

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks On noise abduction for answering counterfactual queries: A practical outlook

Reference 63

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This paper cites Second-Order Uncertainty Quantification: Variance-Based Measures.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Second-Order Uncertainty Quantification: Variance-Based Measures

Reference 64

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Global sensitivity analysis: The primer

Reference 65

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis

Reference 66

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks CXPlain: causal explanations for model interpretation under uncertainty

Reference 67

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Toward causal representation learning

Reference 68

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 69

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Reference 70

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Weakly supervised disentangled generative causal representation learning

Reference 71

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Learning important features through propagating activation differences

Reference 72

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Semi-autoregressive energy flows: exploring likelihood-free training of normalizing flows

Reference 73

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 74

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks On the distribution of points in a cube and the approximate evaluation of integrals

Reference 75

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates

Reference 76

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Unresolved cited work

Reference 77

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Conditional variable importance for random forests

Reference 78

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Axiomatic attribution for deep networks

Reference 79

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Tunkiel, Dan Sui, and Tomasz Wiktorski

Reference 80

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Interpretable counterfactual explanations guided by prototypes

Reference 81

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This paper cites Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Reference 82

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This paper cites Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Reference 83

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Contrastive-ace: Domain generalization through alignment of causal mechanisms

Reference 84

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? In Robin J

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:08.769007Z

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This paper cites Indeterminacy in generative models: Characterization and strong identifiability.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Indeterminacy in generative models: Characterization and strong identifiability

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:08.745373Z

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Observation 85a1b7cd-ab4f-45a7-9ed8-6a18a6839bd8 · outbound

This paper cites Class specific interpretability in cnn using causal analysis.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Class specific interpretability in cnn using causal analysis

Reference 87

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On Measuring Intrinsic Causal Attributions in Deep Neural Networks Global model interpretation via recursive partitioning

Reference 88

Resolution: verified exact; raw_fallback, observed 2026-08-15T21:43:07.330988Z

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Observation 823cfa6c-43e7-4b80-bd2f-44adfde59de2 · outbound

This paper cites Causalvae: Disentangled representation learning via neural structural causal models.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causalvae: Disentangled representation learning via neural structural causal models

Reference 89

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This paper cites Zeiler and Rob Fergus.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Zeiler and Rob Fergus

Reference 90

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This paper cites Causal discovery with reinforcement learning.

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Causal discovery with reinforcement learning

Reference 91

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