@article{ 10.1093/biomet/asz055,
author = "Wang, Y Samuel and Drton, Mathias",
title = "High-dimensional causal discovery under non-Gaussianity",
journal = "Biometrika",
year = "2020",
volume = "107",
month = "Mar",
number = "1",
pages = "41–59",
abstract = "We consider graphical models based on a recursive system of linear structural equations. This implies that there is an ordering, σ, of the variables such that each observed variable Yv is a linear function of a variable-specific error term and the other observed variables Yu with σ(u)<σ(v). The causal relationships, i.e., which other variables the linear functions depend on, can be described using a directed graph. It has previously been shown that when the variable-specific error terms are non-Gaussian, the exact causal graph, as opposed to a Markov equivalence class, can be consistently estimated from observational data. We propose an algorithm that yields consistent estimates of the graph also in high-dimensional settings in which the number of variables may grow at a faster rate than the number of observations, but in which the underlying causal structure features suitable sparsity; specifically, the maximum in-degree of the graph is controlled. Our theoretical analysis is couched in the setting of log-concave error distributions.",
language = "en",
issn = "0006-3444",
doi = "10.1093/biomet/asz055",
}