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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… 
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Related topics

4 relations
Bayesian networkCausalityMarkov blanketMarkov property

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Reduction Without Elimination: Mental Disorders as Causally Efficacious Properties
We argue that any account of mental disorders that meets the desideratum of assigning causal efficacy to mental disorders faces… 
2014
2014
Probabilistic Causation and the Markov Condition
On one understanding of probabilistic causation, the probability attaches to the effect—c probabilistically caused e iff c… 
2009
2009
Can Graphical Causal Inference Be Extended to Nonlinear Settings
This paper assesses an extension of the method for graphical causal inference proposed by Spirtes et al. and Pearl to nonlinear… 
2009
2009
Is Determinism More Favorable than Indeterminism for the Causal Markov Condition?
The present text comments on Steel 2005, in which the author claims to extend from the deterministic to the general case, the… 
2007
2007
EPR robustness and the causal Markov condition
It is still a matter of controversy whether the Principle of the Common Cause (PCC) can be used as a basis for sound causal… 
2007
2007
Exchangeability and Invariance: A Causal Theory
Despite the small numbers that appear in the table, L-N invited us to expand the sample size by whatever proportion to make it a… 
2006
2006
From Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition
Daniel Hausman and James Woodward claim to prove that the causal Markov condition, so important to Bayes-nets methods for causal… 
2005
2005
Close Does Count: Evidence of a Proximity Effect in Inference from Causal Knowledge
Close Does Count: Evidence of a Proximity Effect in Inference from Causal Knowledge Russell C. Burnett (rburnett@mscc.huji.ac.il… 
2004
2004
Causal Discovery Using Adaptive Logics. Towards a more realistic heuristics for human causal learning
In this article I propose a logic that allows one to derive causal statements from probabilistic information. Of course, since… 
2000
2000
Measuring Causes Invariance, Modularity and the Causal Markov Condition
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