Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Causal analysis of continuous-valued variables typically uses either autoregressive models or linear Gaussian Bayesian networks with instantaneous effects. Estimation of Gaussian Bayesian networks poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the nonGaussian instantaneous model with autoregressive models. We show that such a nonGaussian model is identifiable without prior knowledge of network structure, and we propose an estimation method shown to be consistent. This approach also points out how neglecting instantaneous effects can lead to completely wrong estimates of the autoregressive coefficients.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu
• Keywords: Autoregressive Models, Gaussian Bayesian Networks, ICML 2008, Instantaneous Effects, Machine Learning