An analytic and empirical comparison of two methods for discovering probabilistic causal relationships

The discovery of causal relationships from empirical data is an important problem in machine learning. In this paper the attention is focused on the inference of probabilistic causal relationships, for which two different approaches, namely Glymour et al.'s approach based on constraints on correlations and Pearl and Verma's approach based on conditional independencies, have been proposed. These methods differ both in the kind of constraints they consider while selecting a causal model and in the way they search the model which better fits to the sample data. Preliminary experiments show that they are complementary in several aspects. Moreover, the method of conditional independence can be easily extended to the case in which variables have a nominal or ordinal domain. In this case, symbolic learning algorithms can be exploited in order to derive the causal law from the causal model.