User: Guest  Login
Document type:
Zeitschriftenaufsatz 
Author(s):
Wang, Y Samuel; Drton, Mathias 
Title:
High-dimensional causal discovery under non-Gaussianity 
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. 
Dewey Decimal Classification:
510 Mathematik 
Journal title:
Biometrika 
Year:
2020 
Journal volume:
107 
Year / month:
2020-03 
Quarter:
1. Quartal 
Month:
Mar 
Journal issue:
Pages contribution:
41–59 
Language:
en 
Fulltext / DOI:
Publisher:
Oxford University Press (OUP) 
Print-ISSN:
0006-3444 
E-ISSN:
0006-34441464-3510 
Date of publication:
01.03.2020 
Semester:
WS 19-20 
Format:
Text