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Cited by 123Bayesian Modeling and Causal InferenceAdvanced Causal Inference TechniquesGaussian Processes and Bayesian Inference

The DOI matches its Crossref record; 60 of the 66 references it cites were found in a registry or an index.

Learning Functional Causal Models with Generative Neural Networks

Open access
Goudet, Olivier, Kalainathan, Diviyan, Caillou, Philippe, Guyon, Isabelle, Lopez-Paz, David, Sebag, Michèle·2018·The Springer Series on Challenges in Machine Learning
DOI 10.1007/978-3-319-98131-4_3
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Crossref · 10.1007/978-3-319-98131-4_3

Cites

60 of 60 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 66 references

Research works printed but not linked: 8

  1. Chickering, D. M. (2002). Optimal structure identification with greedy search. Journal of Machine Learning Research, 3(Nov):507–554.
  2. Daniusis, P., Janzing, D., Mooij, J., Zscheischler, J., Steudel, B., Zhang, K., and Schölkopf, B. (2012). Inferring deterministic causal relations. arXiv preprint arXiv:1203.3475. · arXiv
  3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Neural Information Processing Systems (NIPS), pages 2672–2680.
  4. Guyon, I. (2013). Chalearn cause effect pairs challenge.
  5. Guyon, I. (2014). Chalearn fast causation coefficient challenge.
  6. Kingma, D. P. and Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. · arXiv
  7. Lopez-Paz, D. and Oquab, M. (2016). Revisiting classifier two-sample tests. arXiv preprint arXiv:1610.06545. · arXiv
  8. Pearl, J. and Verma, T. (1991). A formal theory of inductive causation. University of California (Los Angeles). Computer Science Department.

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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
ORCID
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
DOI
10.1007/978-3-319-98131-4_3
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
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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