Popularity　bias　is　a　common　problem　in　recommender　systems.　Existing　research　mainly　tracks　this　problem　by　re-weighting　training　samples　or　leveraging　a　small　fraction　of　unbiased　data.　However,　the　effect　of　popularity　bias　in　user　behavior　data　may　lead　to　sacrifices　in　recommendation.　In　this　paper,　we　exploit　data　bias　from　click　behavior　to　derive　popularity　bias　representation,　and　investigate　how　to　mitigate　its　negative　impact　from　a　causal　perspective.　Motivated　by　causal　effects,　we　propose　a　novel　counterfactual　inference　framework　named　Mitigating　Popularity　Bias　in　Recommendation　via　Counterfactual　Inference　(MPCI),　which　enables　us　to　capture　the　popularity　bias　as　the　direct　causal　effect　of　the　prediction　score,　and　we　eliminate　popularity　bias　by　subtracting　the　direct　popularity　bias　effect　from　the　total　causal　effect.　In　this　way,　MPCI　reduces　popularity　bias　by　decreasing　the　influence　of　popular　items　on　model　training.　Extensive　experiments　on　two　real-world　datasets　demonstrate　the　superiority　of　our　methods　over　some　strong　baselines　and　prove　the　effectiveness　of　mitigating　popularity　bias　in　recommender　systems.