High-dimensional causal discovery under non-Gaussianity Abstract: Summary We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, $\sigma$, of the variables such that each observed variable $Y_v$ is a linear function of a variable-specific error term and the other observed variables $Y_u$ with $\sigma(u)

Y Samuel Wang and Mathias Drton

Biometrika, 2020, vol. 107, issue 1, 41-59

Keywords: Causal discovery; Directed graphical model; High-dimensional statistics; Non-Gaussian data; Structural equation model (search for similar items in EconPapers)
Date: 2020
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