D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Open accessAbstract
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.
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Robust agents learn causal world models
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“Causal discovery problems such as this are well understood in many settings (Vowels et al., 2022), and in general identifying this causal structure (e.g. that Y → X in Figure 5 a)) is impossible without interventional data and/or additional assumptions.”
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- Authors
- Vowels, M. J., Camgoz, N. C., & Bowden, R.
- Affiliations
- University of Surrey (GB)
- CVSSP, University of Surrey, Guildford, U.K.
- Venue
- ACM Computing Surveys
- Published
- 2022-11-21
- Volume
- 55
- Issue
- 4
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3133932964
- Cited by
- 237
- References
- 8
- Citations by year
- 2026: 32 · 2025: 54 · 2024: 59 · 2023: 57 · 2022: 20 · 2021: 15
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- causal discovery, directed acyclic graphs, causal inference, continuous optimization, benchmark dataset
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