A Study of Causal Discovery with Weak Links and Small Samples

Abstract

Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the influence of weak causal links and varying sample sizes on the discovery of causal models. The experimental results illustrate the effect of larger sample sizes for discovering causal models reliably and the relevance of the strength of causal links and the complexity of the original causal model. We 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-CI (the MML Causal Inducer) causal discovery system finds better models than TETRAD II given small samples from linear causal models. The experimental results also reveal that MML-CI finds weak links with smaller sample sizes than can TETRAD II. 1 Introduction Our research on automating causal discovery aims at develop...