By Y. Samuel Wang and Mathias Drton

We consider recovering causal structure from multivariate observational data. We assume the data arise from a linear structural equation model (SEM) in which the idiosyncratic errors are allowed to be dependent in order to capture possible latent confounding. Each SEM can be represented by a graph where vertices represent observed... Show more

November 9, 2021

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Causal Discovery with Unobserved Confounding and non-Gaussian Data

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