Algebraic Constraints for Linear Acyclic Causal Models

Abstract

In this paper we study the space of second- and third-order moment tensors of random vectors which satisfy a Linear Non-Gaussian Acyclic Model (LiNGAM). In such a causal model each entry $X_i$ of the random vector $X$ corresponds to a vertex $i$ of a directed acyclic graph $G$ and can be expressed as a linear combination of its direct causes $\{X_j:j\to i\}$ and random noise. For any directed acyclic graph $G$, we show that a random vector $X$ arises from a LiNGAM with graph $G$ if and only if certain easy-to-construct matrices, whose entries are second- and third-order moments of $X$, drop rank. This determinantal characterization extends previous results proven for polytrees and generalizes the well-known local Markov property for Gaussian models.

19 pages, 5 figures