The Causal Effect is a numerical measure used to uncover and analyze causal relationships between variables. In particular, it deals with interventions on variables and their effects in the broad areas of observational studies and structural causality. So far, only preliminary attempts have been made to use causal effect as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score as a numerical measure of explanatory relevance for database tuples in relation to queries posed to classical and probabilistic databases.