The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
Influence Diagrams for Causal Modelling and Inference
Abstract
Summary We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decision,etc., we obtain a generalisation of influence diagrams that supports meaningful causal modelling and inference, and only requires concepts and methods that are already standard in the purely probabilistic case. We relate our representations to others, particularly functional models, and present arguments and examples in favour of their superiority.
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“The unification of the graphical, potential outcome, structural equations, decision analytical (Dawid, 2002), interventional (Woodward, 2003), sufficient component (Rothman, 1976) and probabilistic (Suppes, 1970) approaches to causation; with each approach viewed as a restricted version of the SCM.”
From Causal inference in statistics: An overview (2009) · cites this paper as Dawid, 2002
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Formal tools for handling evidence
Leucari, V. (2006)
A model for the evaluation of graduates' first long-term job on labour market history
Romeo, I. (2011) · BOA (University of Milano-Bicocca)
Causal Influence Diagrams for Decision-making
Hu, X., -, J. F., & Xia, W. (2013) · Journal of Convergence Information Technology · doi:10.4156/jcit.vol8.issue5.46
Maclaren, O. J., Parker, A., Pin, C., Carding, S. R., Watson, A. J. M., Fletcher, A. G., Byrne, H. M., & Maini, P. K. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/072561
Doing Ethics in Food and Agriculture
Stannard, C. (2008) · The international library of environmental, agricultural and food ethics/The International library of environmental, agricultural and food ethics · doi:10.1007/978-1-4020-8722-6_2
von Neumann-Morgenstern and Savage Theorems for Causal Decision MakingPreprint
Gonzalez-Soto, M., Sucar, L. E., & Escalante, H. J. (2019) · arXiv (Cornell University)
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Fazia, T., Egidi, L., Ayoglu, B., Beecham, A. H., Bitti, P. P., Ticca, A., Guo, H., McCauley, J. L., Nilsson, P., Asselta, R., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04872
Issues on the Estimation of Latent Variable and Latent Class Models with Social Science Applications
Pennoni, F. (2004) · BOA (University of Milano-Bicocca)
Causal analysis of education and birth inequalities through a latent class structural equation model
Bacci, S., Bartolucci, F., & Pieroni, L. (2012)
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Yu, X., & Smith, J. Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01129
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Taroni, F., Biedermann, A., Bozza, S., Garbolino, P., & Aitken, C. G. G. (2014) · doi:10.1002/9781118914762.refs
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Epistemology of Causal Modelling
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Assessing Causal Relationships in Genomics
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Diagramas de influência: uma aplicação em Jurimetria
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Hoang, Q. A. D. (2021) · theses.fr (ABES) · doi:10.70675/cb165f23zed2fz45d8zb648z3a7021dc5126
A survey of graphical models for decision-making: integrating causality and game theory
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Metadata
- Authors
- Dawid, A. P.
- Affiliations
- University College London (GB)
- Corresponding author
- Dawid, A. P.
- ORCID
- Dawid, A. P.
- Venue
- International Statistical Review
- Publisher
- Wiley
- Published
- 2002-08
- Volume
- 70
- Issue
- 2
- Pages
- 161-189
- ISSN
- 0306-7734 (ISSN-L)
- 1751-5823
- OpenAlex
- W2169315100
- Cited by
- 230
- References
- 40
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 11 · 2020: 4 · 2019: 4 · 2018: 7 · 2017: 3 · 2016: 11 · 2015: 6 · 2014: 9 · 2013: 11 · 2012: 24
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Statistical Review (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- influence diagrams, causal models, causal inference, probabilistic graphical models, decision models, functional model, decision analysis
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