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[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention
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“Lemma 1. (The back-door criterion: G -admissibility) Let G be a directed acyclic graph (DAG) that encodes the causal relationships between variables in a problem, observables as well as unobservable. A sufficient condition for a subset S of covariates to be admissible is that it satisfies the following two conditions (Pearl, 1993):”
From Confounding Equivalence in Causal Inference (2014) · cites this paper as Pearl, 1993
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Causal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336
Self-citationThinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002
The seven tools of causal inference, with reflections on machine learning
Pearl, J. (2019) · Communications of the ACM · doi:10.1145/3241036 · Cited by 659
Self-citationCausal inference and the data-fusion problem
Bareinboim, E., & Pearl, J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510507113 · Cited by 621
Self-citationCausal Interpretations of Black-Box Models
Zhao, Q., & Hastie, T. J. (2019) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2019.1624293 · Cited by 490
Metadata
- Authors
- Pearl, J.
- Corresponding author
- Pearl, Judea
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1993-08-01
- Volume
- 8
- Issue
- 3
- Pages
- 266-269
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W2008312103
- Cited by
- 258
- References
- 0
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 24 · 2023: 15 · 2022: 27 · 2021: 15 · 2020: 13 · 2019: 8 · 2018: 13 · 2017: 5 · 2016: 8 · 2015: 6 · 2014: 13 · 2013: 7 · 2012: 8
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- Bayesian networks, probabilistic graphical models, causality, intervention, expert systems
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