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The Role of Causality in Explainable Artificial Intelligence
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Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations
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Causal Explanation of Convolutional Neural Networks
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Explainable artificial intelligence (XAI) in finance: a systematic literature review
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Scientific Exploration and Explainable Artificial Intelligence
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Explainable AI and Causal Understanding: Counterfactual Approaches Considered
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Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature
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Lorentzian causality theory
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Explainable AI and Law: An Evidential Survey
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Time series prediction of tunnel boring machine (TBM) performance during excavation using causal explainable artificial intelligence (CX-AI)
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Explainable Artificial Intelligence in education
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Exploiting causality signals in medical images: A pilot study with empirical results
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Explainable artificial intelligence in disaster risk management: Achievements and prospective futures
inferredcites →https://doi.org/10.1016/j.inffus.2019.12.012openalex-snapshot
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.003openalex-snapshot
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications
inferredcites →https://doi.org/10.1016/j.inffus.2024.102301openalex-snapshot
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
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Adversarial attacks and defenses in explainable artificial intelligence: A survey
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Counterfactual formulation of patient-specific root causes of disease
inferredcites →https://doi.org/10.1016/j.media.2022.102470openalex-snapshot
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
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Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
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Explainable artificial intelligence model to predict acute critical illness from electronic health records
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Causal machine learning for predicting treatment outcomes
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
inferredcites →https://doi.org/10.1038/s42256-020-00236-4openalex-snapshot
Drug discovery with explainable artificial intelligence
inferredcites →https://doi.org/10.1038/s42256-024-00797-8openalex-snapshot
A causal perspective on dataset bias in machine learning for medical imaging
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semopy: A Python Package for Structural Equation Modeling
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Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI
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Journal of Physics: Conference Series
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The Book of Why: The New Science of Cause and Effect
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Causes and Explanations: A Structural-Model Approach. Part I: Causes
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Robust causal inference using directed acyclic graphs: the R package ‘dagitty’
inferredcites →https://doi.org/10.1109/access.2018.2870052openalex-snapshot
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains
inferredcites →https://doi.org/10.1109/bigdia53151.2021.9619639openalex-snapshot
Methods and Applications of Causal Reasoning in Medical Field
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International Conference on Pattern Recognition
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Explanations for Occluded Images
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Toward Causal Representation Learning
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Uncertainty in Artificial Intelligence
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Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making
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Agree to Disagree: When Deep Learning Models With Identical Architectures Produce Distinct Explanations
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Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
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Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
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Don’t Just Tell Me, Ask Me: AI Systems that Intelligently Frame Explanations as Questions Improve Human Logical Discernment Accuracy over Causal AI explanations
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Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
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Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box
inferredcites →https://doi.org/10.1146/annurev-statistics-031017-100630openalex-snapshot
Causal Structure Learning
inferredcites →https://doi.org/10.1162/153244303321897717openalex-snapshot
10.1162/153244303321897717
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An Algorithm for Fast Recovery of Sparse Causal Graphs
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Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.14778/3476311.3476345openalex-snapshot
Demonstration of generating explanations for black-box algorithms using Lewis
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Scientific Explanation and the Causal Structure of the World
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Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
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DARPA's Explainable Artificial Intelligence Program
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Responsibility and Blame: A Structural-Model Approach
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Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies
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lavaan : An R Package for Structural Equation Modeling
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Natural Language Generation Challenges for Explainable AI
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Cause and Explanation in Ancient Greek Thought
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Information Structures for Causally Explainable Decisions
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A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence
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Deep Backtracking Counterfactuals for Causally Compliant Explanations
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Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
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semopy 2: A Structural Equation Modeling Package with Random Effects in Python
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Leveraging Causal Relations to Provide Counterfactual Explanations and Feasible Recommendations to End Users
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On the Impossibility of Inferring Causation from Association without Background Knowledge
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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