Randomized　experiment　is　an　important　tool　for　studying　the　Average　Treatment　Effect　(ATE).　This　paper　considers　the　regression　adjustment　estimation　of　the　Sample　Average　Treatment　Effect　(SATE)　in　high-dimensional　case,　where　the　multicollinearity　problem　is　often　encountered　and　needs　to　be　properly　handled.　Many　existing　regression　adjustment　methods　fail　to　achieve　satisfactory　performances.　To　solve　this　issue,　an　Elastic-net　adjusted　estimator　for　SATE　is　proposed　under　the　Rubin　causal　model　of　randomized　experiments　with　multicollinearity　in　high　dimensions.　The　asymptotic　properties　of　the　proposed　SATE　estimator　are　shown　under　some　regularity　conditions,　and　the　asymptotic　variance　is　proved　to　be　not　greater　than　that　of　the　unadjusted　estimator.　Furthermore,　Neyman-type　conservative　estimators　for　the　asymptotic　variance　are　proposed,　which　yields　tighter　confidence　intervals　than　both　the　unadjusted　and　the　Lasso-based　adjusted　estimators.　Some　simulation　studies　are　carried　out　to　show　that　the　Elastic-net　adjusted　method　is　better　in　addressing　collinearity　problem　than　the　existing　methods.　The　advantages　of　our　proposed　method　are　also　shown　in　analyzing　the　dataset　of　HER2　breast　cancer　patients.　(C)　2018　Elsevier　B.V.　All　rights　reserved.