Causal Networks: Semantics and Expressiveness
arXiv:1304.2379
Abstract
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies. Often, such knowledge can be represented efficiently with graphical structures such as undirected graphs and directed acyclic graphs (DAGs). In this paper we show that the graphical criterion called d-separation is a sound rule for reading independencies from any DAG based on a causal input list drawn from a graphoid. The rule may be extended to cover DAGs that represent functional dependencies as well as conditional dependencies.
Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988)
Cited by in corpus (10)
- Disintegration and Bayesian Inversion via String Diagrams
- Graphical Criteria for Efficient Total Effect Estimation via Adjustment in Causal Linear Models
- Minimal sufficient causation and directed acyclic graphs
- A general framework for cyclic and fine-tuned causal models and their compatibility with space-time
- Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study
- Detector signal characterization with a Bayesian network in XENONnT
- Analysing causal structures using Tsallis entropies
- Income, education, and other poverty-related variables: a journey through Bayesian hierarchical models
- What Is a Causal Graph?
- Approximate Implication for Probabilistic Graphical Models