{"title":"布雷曼的两种文化:计量经济学的视角","authors":"G. Imbens, S. Athey","doi":"10.1353/obs.2021.0028","DOIUrl":null,"url":null,"abstract":"Abstract:Breiman's \"Two Cultures\" paper painted a picture of two disciplines, data modeling, and algorithmic machine learning, both engaged in the analyses of data but talking past each other. Although that may have been true at the time, there is now much interaction between the two. For example, in economics, machine learning algorithms have become valuable and widely appreciated tools for aiding in the analyses of economic data, informed by causal/structural economic models.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2021.0028","citationCount":"6","resultStr":"{\"title\":\"Breiman's Two Cultures: A Perspective from Econometrics\",\"authors\":\"G. Imbens, S. Athey\",\"doi\":\"10.1353/obs.2021.0028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract:Breiman's \\\"Two Cultures\\\" paper painted a picture of two disciplines, data modeling, and algorithmic machine learning, both engaged in the analyses of data but talking past each other. Although that may have been true at the time, there is now much interaction between the two. For example, in economics, machine learning algorithms have become valuable and widely appreciated tools for aiding in the analyses of economic data, informed by causal/structural economic models.\",\"PeriodicalId\":74335,\"journal\":{\"name\":\"Observational studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1353/obs.2021.0028\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Observational studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1353/obs.2021.0028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2021.0028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Breiman's Two Cultures: A Perspective from Econometrics
Abstract:Breiman's "Two Cultures" paper painted a picture of two disciplines, data modeling, and algorithmic machine learning, both engaged in the analyses of data but talking past each other. Although that may have been true at the time, there is now much interaction between the two. For example, in economics, machine learning algorithms have become valuable and widely appreciated tools for aiding in the analyses of economic data, informed by causal/structural economic models.