The DOI matches its OpenAlex record; 1 of the 1 references it cites were found in a registry or an index.
Causal discovery with continuous additive noise models
Abstract
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable from the distribution under mild conditions. This constitutes an interesting alternative to traditional methods that assume faithfulness and identify only the Markov equivalence class of the graph, thus leaving some edges undirected. We provide practical algorithms for finitely many samples, RESIT (regression with subsequent independence test) and two methods based on an independence score. We prove that RESIT is correct in the population setting and provide an empirical evaluation.
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Cites
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
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Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016) · arXiv · doi:10.48550/arxiv.1606.06565 · Cited by 1,314
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170
Discovering Causal Signals in Images
Lopez-Paz, D., Nishihara, R., Chintala, S., Scholkopf, B., & Bottou, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.14 · Cited by 209
Methods for causal inference from gene perturbation experiments and validation
Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., & Bühlmann, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510493113 · Cited by 140
Self-citationLearning sparse nonparametric DAGs.
Zheng, X., Dan, C., Aragam, B., Ravikumar, P., & Xing, E. P. (2019) · arXiv · doi:10.48550/arxiv.1909.13189 · Cited by 103
Lamboni, M., & Kucherenko, S. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107519 · Cited by 44
PHYRE: A New Benchmark for Physical Reasoning
Bakhtin, A., van der Maaten, L., Johnson, J., Gustafson, L., & Girshick, R. (2019) · arXiv · doi:10.48550/arxiv.1908.05656 · Cited by 37
Berrevoets, J., Kacprzyk, K., Qian, Z., & van der Schaar, M. (2023) · arXiv · doi:10.48550/arxiv.2303.02186 · Cited by 8
“Making such a structural assumptions, before learning a graph using Peters et al. [73] would make our solution for the practitioner’s problem invalid once again.”
BackgroundMethodInfluential
Metadata
- Authors
- Peters, J., Mooij, J. M., Janzing, D., & Schölkopf, B.
- Venue
- Journal of Machine Learning Research
- Publisher
- The MIT Press
- Published
- 2014-01-01
- Volume
- 15
- Issue
- 1
- Pages
- 2009-2053
- ISSN
- 1532-4435 (ISSN-L)
- DOI
- 10.5555/2627435.2670315
- OpenAlex
- W2132547334
- Cited by
- 263
- References
- 1
- FWCI
- 19.15
- Type
- journal article
- Language
- en
- Open access
- closed
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