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
Citations
Cites
Estimating causal effects of treatments in randomized and nonrandomized studies
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Bayesian inference for causal effects: The role of randomization
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PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · cited 4×
“Fairly general sufficient conditions for estimability are given by Galles & Pearl (1995). but it is not known whether or not these are also necessary.”
BackgroundMethodInfluentialProceedings of the 20th conference on Uncertainty in artificial intelligence
Meek, C., Chickering, M., & Halpern, J. Y. (2004) · Cited by 1,548
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Self-citation
Research works printed but not linked: 9
- Balke A.A., 1994, Proceedings of the 10th Conference on Uncertainty in Artifical Intelligence, 46
- Dawid A.P., 2002, Discussion of Lauritzen & Richardson (2002), Journal of the Royal Statistical Society, Series B, 64
- Frydenberg M., 1990, The chain graph Markov property, Scandinavian Journal of Statistics, 17, 333
- Nilsson D., 2000, Proceedings of the 16th Annual Conference on Uncertainty in Artifical Intelligence, 436
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Cited by
Sorted by citations
Causal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336
“The unification of the graphical, potential outcome, structural equations, decision analytical (Dawid, 2002), interventional (Woodward, 2003), sufficient component (Rothman, 1976) and probabilistic (Suppes, 1970) ap-”
BackgroundMendelian randomization as an instrumental variable approach to causal inference
Didelez, V., & Sheehan, N. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206077743 · Cited by 923
An Introduction to Causal Inference
Pearl, J. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1203 · Cited by 755
Bayesian networks in reliability
Helge Langseth, A., & Portinale, L. (2006) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2005.11.037 · Cited by 572
A New Criterion for Confounder Selection
VanderWeele, T. J., & Shpitser, I. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01619.x · Cited by 434
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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