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We begin with a description of the mathematical foundation of Granger causality for determining the causal strength of pairwise relationships (e.g., the strength of A causing changes in B or conversely, B driving changes in A).
The middle panel provides information about any changes in causality in terms of outgoing causal strength ("source relationship") for each location in the network.
Normality and actual causal strength.
The analysis and comparison of the three ATMS research areas in the classification framework suggest that the causal strength of control strategy research is greater than that of reason maintenance algorithm research.
Conversely the right panel depicts changes in causal strength in terms of incoming relationships (i.e., the average causal strength of other areas driving activity at this location or "causal sink").
In other words, it is a measure of the causal strength of each location upon other areas of the network.
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Since more than one DAG may represent exactly the same set of conditional independence relationships [ 40], given static data without perturbations it is more reliable to infer the causal strengths between proteins, regardless of the direction of these causalities.
Several factors suggest that these findings may be causal: the strength and trend of the associations observed; the restriction of the findings to mono- ortho PCBs that have a relatively planar, dioxin-like structure; the existence of similar findings in tumors from other organs; the plausibility of carcinogenic mechanisms; and the association with specific mutation types.
The weights represent direct causal effect strengths.
For a given DAG with edge weights that represent direct causal effect strengths we can generate artificial gene expression data (Details are given in the Supplementary Materials Section 3.1).
Using conventional epidemiologic criteria for inferring causal associations, including strength and consistency of the relationship, biological plausibility, and the possibility of bias as an explanation, it is concluded that the evidence is strongly suggestive that such radiation is carcinogenic.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com