Recently,　recommender　systems　have　witnessed　the　fast　evolution　of　Internet　services.　However,　it　suffers　hugely　from　inherent　bias　and　sparsity　issues　in　interactions.　The　conventional　uniform　embedding　learning　policies　fail　to　utilize　the　imbalanced　interaction　clue　and　produce　suboptimal　representations　to　users　and　items　for　recommendation.　Towards　the　issue,　this　work　is　dedicated　to　bias-aware　embedding　learning　in　a　decomposed　manner　and　proposes　a　counterfactual　graph　convolutional　learning　(CGCL)　model　for　personalized　recommendation.　Instead　of　debiasing　with　uniform　interaction　sampling,　we　follow　the　natural　interaction　bias　to　model　users＇　interests　with　a　counterfactual　hypothesis.　CGCL　introduces　bias-aware　counterfactual　masking　on　interactions　to　distinguish　the　effects　between　majority　and　minority　causes　on　the　counterfactual　gap.　It　forms　multiple　counterfactual　worlds　to　extract　users＇　interests　in　minority　causes　compared　to　the　factual　world.　Concretely,　users　and　items　are　represented　with　a　causal　decomposed　embedding　of　majority　and　minority　interests　for　recommendation.　Experiments　show　that　the　proposed　CGCL　is　superior　to　the　state-of-the-art　baselines.　The　performance　illustrates　the　rationality　of　the　counterfactual　hypothesis　in　bias-aware　embedding　learning　for　personalized　recommendation.