With　the　increasing　growth　of　data　and　the　ability　of　learning　with　them,　machine　learning　models　are　adopted　in　various　domains.　However,　few　of　machine　learning　models　are　able　to　reason　their　prediction,　which　limits　their　further　applications　in　real-world　tasks.　With　the　potential　to　address　this　dilemma,　model　interpretation　has　become　an　important　research　topic　because　of　the　ability　to　provide　the　underlying　reasons　for　model　predictions　at　the　feature　level　or　concept　level.　Model　interpretation　at　the　concept　level　focuses　on　exploring　the　roles　of　concepts　in　model　prediction,　which　enables　more　compact　and　understandable　interpretations.　Concept-level　model　interpretation　requires　the　identification　of　the　concepts　that　contribute　to　model　prediction　and　the　exploration　of　the　rules　underneath　these　concepts.　To　achieve　the　two　objectives,　we　propose　a　Concept-level　Model　Interpretation　framework　(CMIC)　from　the　perspective　of　causality.　CMIC　can　automatically　detect　concepts　in　data　and　discover　the　causal　relation　between　the　detected　concepts　and　the　model's　predicted　labels.　Furthermore,　CMIC　ranks　the　contributions　of　concepts　by　their　causal　effect　on　the　model　prediction,　reflecting　the　detected　concepts'　importance.　We　evaluate　the　proposed　CMIC　framework　on　both　synthetic　and　real-world　datasets　to　demonstrate　the　quality　of　the　provided　interpretation.　IEEE