Statistics Seminar, "Causal structure learning for partially observed multivariate event processes", Niels Richard Hansen, Mathematical Sciences, Copenhagen University
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Structural causal models of event processes imply certain local independencies among the coordinates of the processes. The local independencies form an independence model that can be encoded as a graphical separation model in a directed graph via δ- or μ-separation. If only some of the process coordinates are observed, what can we then learn about the causal structure in terms of the local independence model? We recently showed that independence models given by μ-separation in directed mixed graphs are closed under marginalization, and we characterized the Markov equivalence class of a graph. This naturally leads to a causal structure learning algorithm when a local independence oracle is available. In the talk I will present our result within the context of the general field of graphical models and causal structure learning.