
Identifying Independencies in Causal Graphs with Feedback
We show that the d separation criterion constitutes a valid test for co...
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Nonparametric causal structure learning in high dimensions
The PC and FCI algorithms are popular constraintbased methods for learn...
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On the Testable Implications of Causal Models with Hidden Variables
The validity OF a causal model can be tested ONLY IF the model imposes c...
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On Deducing Conditional Independence from dSeparation in Causal Graphs with Feedback (Research Note)
Pearl and Dechter (1996) claimed that the dseparation criterion for con...
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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 fo...
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Identifying Independence in Relational Models
The rules of dseparation provide a framework for deriving conditional i...
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pdSeparation – A Concept for Expressing Dependence/Independence Relations in Causal Networks
Spirtes, Glymour and Scheines formulated a Conjecture that a direct depe...
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On the Logic of Causal Models
This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relationships from a given causal set of such relationships. As a consequence, dseparation, a graphical criterion for identifying independencies in a DAG, is shown to uncover more valid independencies then any other criterion. In addition, we employ the Armstrong property of conditional independence to show that the dependence relationships displayed by a DAG are inherently consistent, i.e. for every DAG D there exists some probability distribution P that embodies all the conditional independencies displayed in D and none other.
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