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Learning Causal Structure from Undersampled Time Series
2018
Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the "true" underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under
doi:10.1184/r1/6492101.v1
fatcat:lamn4e6nlzfs7jogm4t4zevv6i