{"title":"Estimating Treatment Effect with Propensity Score Weighted Regression and Double Machine Learning","authors":"Jun Xue, Wei Zhong Goh, Dana Rotz","doi":"10.1353/obs.2023.0028","DOIUrl":null,"url":null,"abstract":"Abstract:We applied propensity score weighted regression and double machine learning in the 2022 American Causal Inference Conference Data Challenge. Our double machine learning method achieved the second lowest overall RMSE among all official submissions, but performed less well on heterogeneous treatment effect estimation due to lack of regularization.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"10 6","pages":"83 - 90"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2023.0028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract:We applied propensity score weighted regression and double machine learning in the 2022 American Causal Inference Conference Data Challenge. Our double machine learning method achieved the second lowest overall RMSE among all official submissions, but performed less well on heterogeneous treatment effect estimation due to lack of regularization.
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