An examination on the performance of MML causal induction
Honghua Dai, Gang Li, Ling Zhuang · 2003
This paper presents an examination repOli on the performance of the improved MML based causal model discovery algorithm. In this paper, We firstly describe our improvement to the causal discovery algorithm which introduces a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. It is followed by a detailed examination report on the performance of our improved discovery algorithm. The experimental results of the current version of the discovery system show that: (l) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal networks with large number of variables; (3) the new version works more efficiently compared with the previous version in tenus oftime complexity. Keywords: Causal discovery, causal modelling, inductive inference, machine learning, Bayesian networks, data mining