An entropy based method for causal discovery in linear acyclic model

Abstract

A new algorithm for causal discovery in linear acyclic graphic model is proposed in this paper. The algorithm measures the entropy of observed data sequences by estimating the parameters of its approximate distribution to a generalized Gaussian family. Causal ordering can be discovered by an entropy base method. Compared with previous method, the sample complexity of the proposed algorithm is much lower, which means the causal relationship can be correctly discovered by a smaller number of samples. An explicit requirement of data sequences for correct causal inference in linear acyclic graphic model is discussed. Experiment results for both artificial data and real-world data are presented. © Springer-Verlag 2013.