Learning Linear Non-Gaussian Graphical Models with Multidirected Edges

10/11/2020
by   Yiheng Liu, et al.
0

In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning multidirected edges rather than only directed and bidirected ones. Multidirected edges appear when more than two of the observed variables have a hidden common cause. We detect the presence of such hidden causes by looking at higher order cumulants and exploiting the multi-trek rule. Our method recovers the correct structure when the underlying graph is a bow-free acyclic mixed graph with potential multi-directed edges.

READ FULL TEXT

page 1

page 2

page 3

page 4

08/23/2021

Symmetries in Directed Gaussian Graphical Models

We define Gaussian graphical models on directed acyclic graphs with colo...
01/28/2020

Multi-trek separation in Linear Structural Equation Models

Building on the theory of causal discovery from observational data, we s...
11/19/2021

Analysis of an interventional protein experiment using a vine copula based structural equation model

While there is considerable effort to identify signaling pathways using ...
12/20/2021

Third-Order Moment Varieties of Linear Non-Gaussian Graphical Models

In this paper we study linear non-Gaussian graphical models from the per...
11/28/2019

Constraints in Gaussian Graphical Models

In this paper, we consider the problem of finding the constraints in bow...
11/01/2021

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

Acyclic model, often depicted as a directed acyclic graph (DAG), has bee...
11/01/2021

Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers

Directed acyclic graph (DAG) models are widely used to represent causal ...