Discovering Cyclic and Acyclic Causal Models by Independent Components Analysis

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...

On Causal Discovery with Cyclic Additive Noise Models

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise. We prove that the causal graph of such models is generically identifiable in the bivariate, Gaussian-noise case. We also propose a method to learn such models from observational data. In the acyclic case, the method reduces to ordinary regression, but in...

Discovering causal relations among latent variables in directed acyclic graphical models

Discovering cyclic causal structure

This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represe...

Discovering causal structures in binary exclusive-or skew acyclic models

Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to discover an identifiable causal structure have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limited to continuous data. In this paper, we present a novel causal model f...