
Deciding Morality of Graphs is NPcomplete
In order to find a causal explanation for data presented in the form of ...
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Complexity as Causal Information Integration
Complexity measures in the context of the Integrated Information Theory ...
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On the Logic of Causal Models
This paper explores the role of Directed Acyclic Graphs (DAGs) as a repr...
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Optimizing Causal Orderings for Generating DAGs from Data
An algorithm for generating the structure of a directed acyclic graph fr...
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Causal motifs and existence of endogenous cascades in directed networks with application to company defaults
Motivated by detection of cascades of defaults in economy, we developed ...
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On the Equivalence of Causal Models
Scientists often use directed acyclic graphs (days) to model the qualita...
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On characterizing Inclusion of Bayesian Networks
Every directed acyclic graph (DAG) over a finite nonempty set of variab...
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An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation
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 existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.
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