The discovery of causal models with small samples

The paper examines the influence of sample size on the discovery of causal models. The experimental results illustrate the effect of larger sample sizes for reliably discovering causal models and the relevance of the strength of causal links and the complexity of the original causal model. They present indicative evidence of the superior robustness of MML (minimum message length) methods to standard significance tests in the recovery of causal links. The comparative results show that the MML causal discovery system derives a more reliable model than TETRAD II from a given data set from small samples