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Causal Markov condition

The Markov condition (sometimes called Markov assumption) for a Bayesian network states that any node in a Bayesian network is conditionally… Expand
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2012
2012
We present methods able to predict the presence and strength of conditional and unconditional dependencies (correlations) between… Expand
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Highly Cited
2010
Highly Cited
2010
Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing… Expand
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2010
2010
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences… Expand
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2006
2006
Hausman & Woodward present an argument for the Causal Markov Condition (CMC) on the basis of a principle they dub 'modularity… Expand
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2005
2005
The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical… Expand
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2004
2004
This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We… Expand
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2004
2004
In this article I propose a logic that allows one to derive causal statements from probabilistic information. Of course, since… Expand
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Highly Cited
2002
Highly Cited
2002
In their rich and intricate paper 'Independence, Invariance, and the Causal Markov Condition', Daniel Hausman and James Woodward… Expand
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Highly Cited
1999
Highly Cited
1999
This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been… Expand
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1996
1996
This paper provides a priori cirteria for determing when a causal model is sufficiently complete to be considered a Bayesian… Expand
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