  Causal relations (If A, then B) are prevalent in requirements artifacts.
Automatically extracting causal relations from requirements holds great
potential for various RE activities (e.g., automatic derivation of suitable
test cases). However, we lack an approach capable of extracting causal
relations from natural language with reasonable performance. In this paper, we
present our tool CATE (CAusality Tree Extractor), which is able to parse the
composition of a causal relation as a tree structure. CATE does not only
provide an overview of causes and effects in a sentence, but also reveals their
semantic coherence by translating the causal relation into a binary tree. We
encourage fellow researchers and practitioners to use CATE at
https://causalitytreeextractor.com/
