An Information-theoretic Approach to Causal Power

The measurement of causal power has been of long-standing interest in philosophy and psychology. With the development of Bayesian network technology, the interest has spread to artificial intelligence. We introduce a metric which applies information theory to Bayesian networks. We show that it generalizes prior metrics restricted to linear and noisy-or models, while providing a metric appropriate to the full representational power of Bayesian nets.