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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

Related topics

4 relations
Bayesian networkCausalityMarkov blanketMarkov property

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Quantum causal models via quantum Bayesianism
Proposals for quantum causal models have tended to favour a mechanistic view, according to which classical causal graphs should… 
2017
2017
Interactive Causes: Revising the Markov Condition
This article suggests a revision of the theory of causal nets (TCN). In section 1 we introduce an axiomatization of TCN based on… 
Review
2015
Review
2015
Causal Bayes nets as psychological theories of causal reasoning: evidence from psychological research
Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal… 
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… 
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… 
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… 
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… 
2004
2004
Manipulation and the Causal Markov Condition
This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We… 
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… 
1996
1996
The causal Markov condition, fact or artifact?
This paper provides a priori cirteria for determing when a causal model is sufficiently complete to be considered a Bayesian… 
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