Linear Causal Model discovery using the MML criterion

Abstract

Determining the causal structure of a domain is a key task in the area of data mining and knowledge discovery. The algorithm proposed by Wallace et al. (1996) 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 occasionally 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.