[Context:] Causal relations (e.g., If A, then B) are prevalent in functional\nrequirements. For various applications of AI4RE, e.g., the automatic derivation\nof suitable test cases from requirements, automatically extracting such causal\nstatements are a basic necessity. [Problem:] We lack an approach that is able\nto extract causal relations from natural language requirements in fine-grained\nform. Specifically, existing approaches do not consider the combinatorics\nbetween causes and effects. They also do not allow to split causes and effects\ninto more granular text fragments (e.g., variable and condition), making the\nextracted relations unsuitable for automatic test case derivation. [Objective &\nContributions:] We address this research gap and make the following\ncontributions: First, we present the Causality Treebank, which is the first\ncorpus of fully labeled binary parse trees representing the composition of\n1,571 causal requirements. Second, we propose a fine-grained causality\nextractor based on Recursive Neural Tensor Networks. Our approach is capable of\nrecovering the composition of causal statements written in natural language and\nachieves a F1 score of 74 % in the evaluation on the Causality Treebank. Third,\nwe disclose our open data sets as well as our code to foster the discourse on\nthe automatic extraction of causality in the RE community.\n