{"title":"营销应用中的因果推理","authors":"Peter E. Rossi","doi":"10.2139/ssrn.3035502","DOIUrl":null,"url":null,"abstract":"Marketing applications offer many difficult and unique challenges in causal inference. In particular, targeted marketing activities, the arch-typical example of is search ads, can be difficult to evaluate using purely observational data. I review causal methods proposed in the recent econometrics literature and consider their suitability for various problems in marketing. In particular, I call attention to problems of how to evaluate various estimation procedures in the marketing context.","PeriodicalId":209879,"journal":{"name":"MKTG: Marketing Mix Decisions (Topic)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Causal Inference in Marketing Applications\",\"authors\":\"Peter E. Rossi\",\"doi\":\"10.2139/ssrn.3035502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Marketing applications offer many difficult and unique challenges in causal inference. In particular, targeted marketing activities, the arch-typical example of is search ads, can be difficult to evaluate using purely observational data. I review causal methods proposed in the recent econometrics literature and consider their suitability for various problems in marketing. In particular, I call attention to problems of how to evaluate various estimation procedures in the marketing context.\",\"PeriodicalId\":209879,\"journal\":{\"name\":\"MKTG: Marketing Mix Decisions (Topic)\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MKTG: Marketing Mix Decisions (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3035502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MKTG: Marketing Mix Decisions (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3035502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Marketing applications offer many difficult and unique challenges in causal inference. In particular, targeted marketing activities, the arch-typical example of is search ads, can be difficult to evaluate using purely observational data. I review causal methods proposed in the recent econometrics literature and consider their suitability for various problems in marketing. In particular, I call attention to problems of how to evaluate various estimation procedures in the marketing context.