Linear Causal Model Discovery Using the MML criterion Gang Li, Honghua Dai, and Yiqing Tu   View Paper (PDF)   Return to Main-Track Regular Papers Abstract has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasion-ally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision. DiSC'03 © 2003 Association for Computing Machinery