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The regulator nodes are causally linked to the true state nodes as determined by the causal graph.
This naturally raises the question of whether planning is easier when the causal graph is acyclic.
These failure modes have been quantified according to different parameters, and prioritized by a causal graph.
The paper considers application of causal graph to description of diagnosed process.
We then present a new characterization of weak cause for a certain class of causal models in which the causal graph over the endogenous variables has the form of a directed chain of causal subgraphs, called decomposable causal graph.
Furthermore, we also identify two important subclasses in which the causal graph over the endogenous variables forms a directed tree and more generally a directed chain of layers, called causal tree and layered causal graph, respectively.
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In this paper, a new causal graph-based fault detection and isolation framework is developed.
1) Causal graph-based methods rely on certain assumptions.
Out of the latter 20 SNPs, only one (rs6910071) is found by the causal graph-based method in our study.
In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data.
In this paper we perform a case study for applicability of causal graph-based framework to analysis of GWAS data.
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