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A linear Gaussian parameterization of a causal model has the advantage that one can characterize the causal effect of an individual pathway, and that the causal effect from one variable on another decomposes into the causal effects of each connecting pathway, which themselves decompose into the caus ...

The now widely used theory of causal graphical models considers causal relations among a set of statistical variables. The causal relations are represented in terms of a directed graph among the set of variables, and the task of causal discovery is to identify this causal structure on the basis of t ...