This paper addresses the challenge of uniquely determining the causal order in probabilistic logic programs learned from data, where probabilistic information alone can lead to ambiguity. By exploiting the relationship with Bayesian networks and incorporating relational structure constraints, it provides conditions under which a unique causal order is established, thereby enabling well-defined intervention semantics.