
On the SemiMarkov Equivalence of Causal Models
The variability of structure in a finite Markov equivalence class of cau...
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A Local Method for Identifying Causal Relations under Markov Equivalence
Causality is important for designing interpretable and robust methods in...
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Causal Networks: Semantics and Expressiveness
Dependency knowledge of the form "x is independent of y once z is known"...
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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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Causal Identification under Markov Equivalence
Assessing the magnitude of causeandeffect relations is one of the cent...
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Causal Modeling
Causal Models are like Dependency Graphs and Belief Nets in that they pr...
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Causal Discovery from Changes
We propose a new method of discovering causal structures, based on the d...
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On the Equivalence of Causal Models
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 causal models are equivalent if there is no experiment which could distinguish one from the other. A canonical representation for causal models is presented which yields an efficient graphical criterion for deciding equivalence, and provides a theoretical basis for extracting causal structures from empirical data. This representation is then extended to the more general case of an embedded causal model, that is, a dag in which only a subset of the variables are observable. The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information. This algorithm leads to a model theoretic definition of causation in terms of statistical dependencies.
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