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Learning Functional Causal Models with Generative Neural Networks
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“It was also shown that the use of counterfactual explanations can help the user to understand the decision of a model [40, 42, 351].”
From Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI (2020) · cites this paper as [40]
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Metadata
- Authors
- Goudet, O., Kalainathan, D., Caillou, P., Guyon, I., Lopez-Paz, D., & Sebag, M.
- Affiliations
- Université Paris-Sud (FR)
- Corresponding author
- Goudet, Olivier
- Venue
- The Springer Series on Challenges in Machine Learning
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 39-80
- ISSN
- 2520-131X (ISSN-L)
- 2520-1328
- OpenAlex
- W2755310417
- Cited by
- 123
- References
- 66
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 15 · 2023: 14 · 2022: 19 · 2021: 12 · 2020: 18 · 2019: 12 · 2018: 10
- FWCI
- 19.46
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The Springer series on challenges in machine learning (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- causal discovery, generative neural networks, maximum mean discrepancy, observational data, conditional independence, directed acyclic graphs, confounders, latent variables
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