This　paper　is　concerned　with　model　averaging　procedure　for　varying-coefficient　partially　linear　models　with　missing　responses.　The　profile　least-squares　estimation　process　and　inverse　probability　weighted　method　are　employed　to　estimate　regression　coefficients　of　the　partially　restricted　models,　in　which　the　propensity　score　is　estimated　by　the　covariate　balancing　propensity　score　method.　The　estimators　of　the　linear　parameters　are　shown　to　be　asymptotically　normal.　Then　we　develop　the　focused　information　criterion,　formulate　the　frequentist　model　averaging　estimators　and　construct　the　corresponding　confidence　intervals.　Some　simulation　studies　are　conducted　to　examine　the　finite　sample　performance　of　the　proposed　methods.　We　find　that　the　covariate　balancing　propensity　score　improves　the　performance　of　the　inverse　probability　weighted　estimator.　We　also　demonstrate　the　superiority　of　the　proposed　model　averaging　estimators　over　those　of　existing　strategies　in　terms　of　mean　squared　error　and　coverage　probability.　Finally,　our　approach　is　further　applied　to　a　real　data　example.　(C)　2018　The　Korean　Statistical　Society.　Published　by　Elsevier　B.V.　All　rights　reserved.