In　this　article,　based　on　the　covariate　balancing　propensity　score　(CBPS),　estimators　for　the　regression　coefficients　and　the　population　mean　are　obtained,　when　the　responses　of　linear　models　are　missing　at　random.　It　is　proved　that　the　proposed　estimators　are　asymptotically　normal.　In　simulation　studies　and　real　example,　the　proposed　estimators　show　improved　performance　relative　to　usual　augmented　inverse　probability　weighted　estimators.