The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
An Algorithm for Fast Recovery of Sparse Causal Graphs
Abstract
Previous asymptotically correct algorithms for recovering causal structure from sample probabilities have been limited even in sparse causal graphs to a few variables. We describe an asymptotically correct algorithm whose complexity for fixed graph connectivity increases polynomially in the number of vertices, and may in practice recover sparse graphs with several hundred variables. From sample data with n = 20,000, an implementation of the algorithm on a DECStation 3100 recovers the edges in a linear version of the ALARM network with 37 vertices and 46 edges. Fewer than 8% of the undirected edges are incorrectly identified in the output. Without prior ordering information, the program also determines the direction of edges for the ALARM graph with an error rate of 14%. Processing time is less than 10 seconds. Keywords DAGS, Causal Modelling.
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Loehlin, J. C., & Beaujean, A. A. (2016) · doi:10.4324/9781315643199 · Cited by 1,381
Introduction to Structural Equation Models.
Cherlin, A., & Duncan, O. D. (1976) · Contemporary Sociology · doi:10.2307/2064059 · Cited by 1,190
Equivalence and Synthesis of Causal Models
Verma, T., & Pearl, J. (2022) · Probabilistic and Causal Inference · doi:10.1145/3501714.3501732 · Cited by 964
Introduction to Structural Equation Models.
Beniger, J. R., & Duncan, O. D. (1976) · Contemporary Sociology · doi:10.2307/2064058 · Cited by 333
Spirtes, P., Glymour, C., & Scheines, R. (2018) · doi:10.1184/r1/6491090 · Cited by 78
Self-citationConstructor: a system for the induction of probabilistic models
Fung, R. M., & Crawford, S. L. (1990) · Cited by 68
Cited by
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
Self-citationNetwork analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359
Inferring causation from time series in Earth system sciences
Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10105-3 · Cited by 984
Self-citationDetecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979
Learning Bayesian networks from data: An information-theory based approach
Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00191-1 · Cited by 803
Metadata
- Authors
- Spirtes, P., & Glymour, C.
- Venue
- Social Science Computer Review
- Publisher
- SAGE Publications
- Published
- 1991-04
- Volume
- 9
- Issue
- 1
- Pages
- 62-72
- ISSN
- 0894-4393 (ISSN-L)
- 1552-8286
- OpenAlex
- W1975062332
- Cited by
- 953
- References
- 6
- FWCI
- 6.28
- Type
- journal article
- Language
- en
- Open access
- closed
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