Discovering causal structures in binary exclusive-or skew acyclic models

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM

Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to discover a unique causal model have been explored based on nonGaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model for bina...

Discovering causal relations among latent variables in directed acyclic graphical models

Discovering Cyclic and Acyclic Causal Models by Independent Components Analysis

We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. In the large sample limit, LiNG discover...

Discovering Cyclic Causal Models by Independent Components Analysis

We generalize Shimizu et al’s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM’s graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the dis...

Measurement Error and Causal Discovery

Algorithms for causal discovery emerged in the early 1990s and have since proliferated [4, 10]. After directed acyclic graphical representations of causal structures (causal graphs) were connected to conditional independence relations (the Causal Markov Condition1 and dseparation2), graphical characterizations of Markov equivalence classes of causal graphs (patterns) soon followed, along with p...