The annotation of causal relations in natural language texts can lead to a low inter-annotator agreement. A French corpus annotated with causal relations would be helpful for the evaluation of programs that extract causal knowledge, as well as for the study of the expression of causation. As previous theoretical work provides no necessary and sufficient condition that would allow an annotator to easily identify causation, we explore features that are associated with causation in human judgements. We present an experiment that allows us to elicit intuitive features of causation. We test the statistical association of features of causation from theoretical previous work with causation itself in human judgements in an annotation experiment. We then establish guidelines based on these features for annotating a French corpus. We argue that our approach leads to coherent annotation guidelines, since it allows us to obtain a κ = 0.84 agreement between the majority of the annotators answers and our own educated judgements. We present these annotation instructions in detail.