106 citations · 126 across the 5 of their papers we have counts for
5 papers
Causal Networks: Semantics and Expressiveness
Tom S. Verma, Judea Pearl
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,…
d-Separation: From Theorems to Algorithms
Dan Geiger, Tom S. Verma, Judea Pearl
An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and co…
On the Equivalence of Causal Models
Tom S. Verma, Judea Pearl
Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data. Two caus…
An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
Tom S. Verma, Judea Pearl
In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the ex…
Deciding Morality of Graphs is NP-complete
Tom S. Verma, Judea Pearl
In order to find a causal explanation for data presented in the form of covariance and concentration matrices it is necessary to decide if the graph formed by such associations is…