Recommender　systems　usually　face　the　bias　problem,　which　creates　a　gap　between　recommendation　results　and　the　actual　user　preference.　Existing　works　track　this　problem　by　assuming　a　specific　bias　and　then　develop　a　method　to　mitigate　it,　which　lack　universality.　In　this　paper,　we　attribute　the　root　reason　of　the　bias　problem　to　a　causality　concept:　confounders,　which　are　the　variables　that　influence　both　which　items　the　user　will　interact　with　and　how　they　rate　them.　Meanwhile,　the　theory　around　causality　says　that　some　confounders　may　remain　unobserved　and　are　hard　to　calculate.　Accordingly,　we　propose　a　novel　Counterfactual　I　nference　for　Deconfounded　Recommendation　(CIDR)　framework　that　enables　the　analysis　of　causes　of　biases　from　a　causal　perspective.　We　firstly　analyze　the　causal-effect　of　confounders,　and　then　utilize　the　biased　observational　data　to　capture　a　substitute　of　confounders　on　both　user　side　and　item　side.　Finally,　we　boost　counterfactual　inference　to　eliminate　the　causal-effect　of　such　confounders　in　order　to　achieve　a　satisfactory　recommendation　with　the　help　of　user　and　item　side　information　(e.g.,　user　post-click　feedback　data,　item　multi-model　data).　For　evaluation,　we　compare　our　method　with　several　state-of-the-art　debias　methods　on　three　real-world　datasets,　in　addition　to　new　causal-based　approaches.　Extensive　experiments　demonstrate　the　effectiveness　of　our　proposed　method.