In multi-label learning (MLL) problems, each example is associated with a set of labels. In order to train a well-performed predictor for unseen examples, exploiting relations between labels is crucially important. Most exiting studies simplify the relation as correlations among labels, typically based on their co-occurrence. This study discloses that causal relations are more essential for describing how a label can help another one during the learning process. Based on this observation, two strategies are proposed to generate causal orders of labels from the label causal directed acyclic graph (DAG), following the constraint that the cause label should be prior to the effect label. The main idea of the first strategy is to sort a random order to make it satisfied the cause-effect relations in DAG. And the main idea of the second strategy is to put labels into many non-intersect topological levels based on the structure of the DAG, then sort these labels through their topological structure. Further, by incorporating the causal orders into the classifier chain (CC) model, an effective MLL approach is proposed to exploit the label relation from a more essential view. Experiments results on multiple datasets validate that the extracted causal order of labels indeed provides helpful information to boost the performance.