
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
It is well known that there may be many causal explanations that are con...
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A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables
Different directed acyclic graphs (DAGs) may be Markov equivalent in the...
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On the SemiMarkov Equivalence of Causal Models
The variability of structure in a finite Markov equivalence class of cau...
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Almost Optimal Intervention Sets for Causal Discovery
We conjecture that the worst case number of experiments necessary and su...
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A generalized backdoor criterion
We generalize Pearl's backdoor criterion for directed acyclic graphs (D...
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Faster algorithms for Markov equivalence
Maximal ancestral graphs (MAGs) have many desirable properties; in parti...
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Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables
We provide a scheme for inferring causal relations from uncontrolled sta...
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A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Meek (1995) characterizes Markov equivalence classes for DAGs (with no latent variables) by presenting a set of orientation rules that can correctly identify all arrow orientations shared by all DAGs in a Markov equivalence class, given a member of that class. For DAG models with latent variables, maximal ancestral graphs (MAGs) provide a neat representation that facilitates model search. Earlier work (Ali et al. 2005) has identified a set of orientation rules sufficient to construct all arrowheads common to a Markov equivalence class of MAGs. In this paper, we provide extra rules sufficient to construct all common tails as well. We end up with a set of orientation rules sound and complete for identifying commonalities across a Markov equivalence class of MAGs, which is particularly useful for causal inference.
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