Doubly　robust　(DR)　methods　that　employ　both　the　propensity　score　and　outcome　models　are　widely　used　to　estimate　the　causal　effect　of　a　treatment　and　generally　outperform　those　methods　only　using　the　propensity　score　or　the　outcome　model.　However,　without　appropriately　chosen　the　working　models,　DR　estimators　may　substantially　lose　efficiency.　In　this　paper,　based　on　the　augmented　inverse　probability　weighting　procedure,　we　derive　a　new　estimating　equation　for　the　causal　effect　by　the　strategy　of　combining　estimating　equations.　The　resulting　estimator　by　solving　the　new　estimating　equation　retains　doubly　robust　and　can　improve　the　efficiency　under　the　misspecification　of　conditional　mean　working　model.　We　further　show　the　large　sample　properties　of　the　proposed　estimator　under　some　regularity　conditions.　Through　simulation　experiments　and　a　real　data　analysis,　we　illustrate　that　the　proposed　method　is　competitive　with　its　competitors,　which　is　in　line　with　those　implied　by　the　asymptotic　theory.