Cited by 230Bayesian Modeling and Causal InferenceLogic, Reasoning, and KnowledgeAdvanced Causal Inference Techniques

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Influence Diagrams for Causal Modelling and Inference

Dawid, A. P.·2002·International Statistical Review

DOI 10.1111/j.1751-5823.2002.tb00354.x

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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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Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
FOUNDATIONS OF STATISTICS
Anderson, N. H. (2001) · Psychology Press eBooks · doi:10.4324/9781410600004-24 · Cited by 6,939
The Foundations of Statistics.
Good, I. J., & Savage, L. J. (1955) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2343133 · Cited by 4,764
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
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
A New Approach To Causal Inference in Mortality Studies With a Sustained Exposure Period - Application To Control of the Healthy Worker Survivor Effect
Robins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451
Causal diagrams for empirical research
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.”
BackgroundMethodInfluential
Proceedings of the 20th conference on Uncertainty in artificial intelligence
Meek, C., Chickering, M., & Halpern, J. Y. (2004) · Cited by 1,548
Equivalence and Synthesis of Causal Models
Verma, T., & Pearl, J. (1990) · Conference on Uncertainty in Artificial Intelligence · doi:10.1145/3501714.3501732 · Cited by 1,542
Conditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507
Self-citation

Show all 40 references

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
Pearl J., 2000, Causality
Pearl J., 1995, Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 444
Raiffa H., 1968, Decision Analysis
Robins J.M., 1989, Health Service Research Methodology: A Focus on Aids, 113
Studeny M.(2001).On non‐graphical description of models of conditional independence structure. Paper presented at HSSS Workshop on Stochastic Systems for Individual Behaviours 22–23 January 2001 Louvain la Neuve Belgium.

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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
DOI: 10.1111/j.1751-5823.2002.tb00354.x
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
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1754 d)
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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Influence Diagrams for Causal Modelling and Inference

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