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This papers develops a logical language for representing probabilistic causal laws. The interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In the formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way.
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