We　propose　a　model　averaging　approach,　combined　with　a　partition　and　matching　method　to　estimate　the　conditional　average　treatment　effects　under　heteroskedastic　error　settings.　The　proposed　approach　has　asymptotic　optimality　and　consistency　of　weights　and　estimator.　Numerical　studies　show　that　our　method　has　good　finite　-sample　performances.