Foundations of Causal Discovery
published: Oct. 12, 2016, recorded: August 2016, views: 1478
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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 the probability distribution generated by the variables in the graph. I will provide an introduction and overview of some of the methods for causal discovery and present known identifiability results with a particular focus on the assumptions they depend on.
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